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-PVDECONV: POINT-VOXEL DECONVOLUTION FOR AUTOENCODING CAD
-CONSTRUCTION IN 3D
-Kseniya Cherenkova?yDjamila Aouada?Gleb Gusevy
-?SnT, University of LuxembourgyArtec3D
-ABSTRACT
-We propose a Point-V oxel DeConvolution ( PVDeConv ) mod-
-ule for 3D data autoencoder. To demonstrate its efficiency we
-learn to synthesize high-resolution point clouds of 10k points
-that densely describe the underlying geometry of Computer
-Aided Design (CAD) models. Scanning artifacts, such as pro-
-trusions, missing parts, smoothed edges and holes, inevitably
-appear in real 3D scans of fabricated CAD objects. Learning
-the original CAD model construction from a 3D scan requires
-a ground truth to be available together with the corresponding
-3D scan of an object. To solve the gap, we introduce a new
-dedicated dataset, the CC3D, containing 50k+ pairs of CAD
-models and their corresponding 3D meshes. This dataset is
-used to learn a convolutional autoencoder for point clouds
-sampled from the pairs of 3D scans - CAD models. The chal-
-lenges of this new dataset are demonstrated in comparison
-with other generative point cloud sampling models trained on
-ShapeNet. The CC3D autoencoder is efficient with respect to
-memory consumption and training time as compared to state-
-of-the-art models for 3D data generation.
-Index Terms —CC3D, point cloud autoencoder, CAD
-models generation, Scan2CAD.
-1. INTRODUCTION
-Recently, deep learning (DL) for 3D data analysis has seen
-a boost in successful and competitive solutions for segmen-
-tation, detection and classification [1], and real-life applica-
-tions, such as self-driving, robotics, medicine, and augmented
-reality. In industrial manufacturing, 3D scanning of fabricated
-parts is an essential step of product quality control, when the
-3D scans of real objects are compared to the original Com-
-puter Aided Design (CAD) models. While most consumer
-solutions for 3D scanning are good enough for capturing
-the general shape of an object, artifacts can be introduced
-in the parts of the object that are physically inaccessible for
-3D scanning, resulting in the loss of sharp features and fine
-details.
-This paper focuses on recovering scanning artifacts in an
-autoencoding data-driven manner. In addition to presenting a
-new point cloud autoencoder, we introduce a new 3D dataset,
-referred to as CC3D , which stands for CAD Construction
-Fig. 1 . Examples of CC3D data: From left to right, CAD
-models, corresponding 3D scans, 10k input point clouds and
-results of the proposed autoencoder.
-in 3D . We further provide an analysis focused on real 3D
-scanned data, keeping in mind real-world constraints, i.e.,
-variability, complexity, artifacts, memory and speed require-
-ments. The first two columns in Fig. 1 give some examples
-from CC3D data; the CAD model and its 3D scanned version
-in triangular mesh format. While the most recent existing
-solutions [2, 3, 4, 5] on 3D data autoencoders mostly focus
-on low-resolution data configuration (approximately 2500
-points), we see it more beneficial for real data to experiment
-in higher-dimension. It is what brings the important 3D object
-details into the big data learning perspective.
-Several publicly available datasets related to CAD mod-
-elling, such as ModelNet [6], ShapeNet [7], and ABC [8],
-have been released in the last years. The summary of the fea-
-tures they offer can be found in Table 1. These datasets have
-boosted the research on deep learning on 3D point clouds
-mainly.
-Similarly, our CC3D dataset should support research ef-
-forts in addressing real-world challenges. Indeed, this dataset
-provides various 3D scanned objects, with their ground-truth
-CAD models. The models collected in CC3D dataset are
-not restricted to any object’s category and/or complexity. 3D
-scans offer challenging cases of missing data, smoothed ge-arXiv:2101.04493v1  [cs.CV]  12 Jan 2021ometry and fusion artefacts in the form of varying protrusions
-and swept holes. Moreover, the resolution of 3D scans is typ-
-ically high with more than 100k faces in the mesh.
-In summary, the contributions of this paper include: (1)
-A 3D dataset, CC3D, a collection of 50k+ aligned pairs of
-meshes, a CAD model and its virtually 3D scanned coun-
-terpart with corresponding scanning artifacts; (2) A CC3D
-autoencoder architecture on 10k point clouds learned from
-CC3D data; (3) A Point-V oxel DeConvolution ( PVDeConv )
-block for the decoder part of our model, combining point fea-
-tures on coarse and fine levels of the data.
-The remainder of the paper is organized as follows: Sec-
-tion 2 reviews relevant state-of-the-art works in 3D data au-
-toencoding. In Section 3 we give a brief overview of the
-core components our work is built upon. Section 4 describes
-the main contributions of this paper in more details. In Sec-
-tion 5 the results and comparison with related methods are
-presented. Section 6 gives the conclusions.
-2. RELATED WORK
-The choice of DL architecture and 3D data representation is
-usually defined by existing practices and available datasets
-for learning [9]. V oxel-based representations have pioneered
-3D data analysis, applying 3D Convolution Neural Network
-(CNN) directly on a regular voxel grid [10]. Despite the im-
-proved models in terms of memory consumption, e.g., [11],
-their inability to resolve fine object details remains the main
-limiting factor in practical use.
-Other works introduce convolutions directly on graph
-structures, e.g., [12]. They attempt to generalize DL models
-to non-Euclidean domains such as graphs and manifolds [13],
-and offer the analogs of pooling/unpooling operations as
-well [14]. However, they are not applicable for learning on
-real unconstrained data as they require either meshes to be
-registered to a common template, or inefficiently deal with
-meshes of up to several thousand faces, or are specific to
-segmentation or classification tasks only.
-Recent advances in developing efficient architectures for
-3D data analysis are mainly related to point cloud based meth-
-ods [15, 16]. Decoders [17, 2, 18, 3, 19] have made point
-clouds a highly promising representation for 3D object gen-
-eration and completion using neural networks. Successful
-works in generative adversarial network (GAN) (e.g.,[20]),
-show the applicability of different GAN models operating on
-the raw point clouds.
-In this paper, we comply with the common autoencoder
-approach, i.e., we use a point cloud encoder to embed the
-point cloud input, and design a decoder to generate a complete
-point cloud from the embedding of the encoder.3. BACKGROUND AND MOTIVATION
-We herein present the fundamental building blocks that com-
-prise the core of this paper, namely, the point cloud, metric on
-it, and the DL backbone. All together, these elements make
-the CC3D autoencoder perform efficiently on high-resolution
-3D data.
-A point cloud Scan be represented as S=f(pk; fk)g,
-where each pkis the 3D coordinates of the kthinput point,
-andfkis the feature corresponding to it, and the size of fk
-defines the dimensionality of the points feature space. Note
-that while it is straightforward to include auxiliary informa-
-tion (such as points’ normals) to our architecture, in this paper
-we exclusively employ the xyzcoordinates of pkas the input
-data.
-We base on Point-V oxel Convolution (PVConv), a mem-
-ory efficient architecture for learning on 3D point cloud pre-
-sented in [21]. To the best of our knowledge, it is the first de-
-velopment of autoencoder based on PVCNN as the encoder.
-Briefly, PVConv combines the fine-grained feature transfor-
-mation on points with the coarse-grained neighboring feature
-aggregation in the voxel space of point cloud. Three basic op-
-erations are performed in the coarse branch, namely, voxeliza-
-tion, followed by voxel-based 3D convolution, and the devox-
-elization. The point-based branch aggregates the features for
-each individual with multilayer perceptron (MLP), providing
-high resolution details. The features from both branches are
-aggregated into a hidden feature representation.
-The formulation of convolution in both voxel-based and
-point-based cases is the following:
-yk=X
-xi2N(xk)K(xk; xi)F(xi); (1)
-where for each center xk, and its neighborhood N(xk), the
-neighboring features F(xi)are convolved with the kernel
-K(xk; xi). The choice of PVCNN is due to its efficiency
-in training on high-resolution 3D data. Indeed, it makes it
-a good candidate for working with real-life data. As it is
-stated in [21], PVConv combines advantages of point-based
-methods, reducing memory consumption, and voxel-based,
-improving the data locality and regularity.
-For the loss function, Chamfer distance [22] is used to
-measure the quality of the autoencoder. It is a differentiable
-metric, invariant to permutation of points in both ground-truth
-and target point clouds, SGandS, respectively. It is defined
-as follows:
-dCD(S; SG) =X
-x2Smin
-y2SGkxyk2+X
-y2SGmin
-x2Skxyk2:
-(2)
-As it follows from its definition, no correspondence or equal
-number of points in SandSGis required for the computation
-ofdCD, making it possible to work within different resolu-
-tions for the encoder and decoder.4. PROPOSED AUTOENCODING OF 3D SCANS TO
-CAD MODELS
-This paper studies the problem of 3D point cloud autoencod-
-ing in a deep learning setup, and in particular, the choice
-of the architecture of a 3D point cloud decoder for efficient
-reconstruction of point clouds sampled from corresponding
-pairs of 3D scans and CAD models.
-4.1. CC3D dataset
-The CC3D dataset of 3D CAD models was collected from a
-free online service for sharing CAD designs [23]. In total,
-the collected dataset contains 50k+ models in STEP format,
-unrestricted to any category, with varying complexity from
-simple to highly detailed designs (see examples in Fig. 1).
-These CAD models are converted to meshes, and each mesh
-was virtually scanned using proprietary 3D scanning pipeline
-developed by Artec3D [24]. The typical size of the result-
-ing scans is in the order of 100K points and faces, while the
-meshes converted from CAD models are usually more than
-an order of magnitude lighter.
-In order to illustrate the uniqueness of our dataset, Ta-
-ble 1 summarizes the available CAD-like datasets and se-
-mantic information they provide. Unlike ShapeNet [7] and
-ModelNet [6], the CC3D dataset is a collection of 3D ob-
-jects unrestricted to any category, with the complexity vary-
-ing from very basic to highly detailed models. One of the
-most recent datasets, the ABC dataset [8] would have been a
-valuable collection due to its size for our task if it had con-
-tained 3D scanned models alongside with ground-truth CAD
-objects. The availability of CAD-3D scan pairings, the high-
-resolution of meshes and variability of the models make the
-CC3D dataset stand out among other alternatives. The CC3D
-dataset will be shared with the research community.
-4.2. CC3D Autoencoder
-Our decoder is a modified version of PVCNN, where we
-cut the final classification/segmentation layer. The proposed
-Dataset #Models
-CAD
-Curves
-Patches
-Semantics
-Categories
-3D scan
-CC3D (ours) 50k+ 3 7 7 7 7 3
-ABC [8] 1M+ 3 3 3 7 7 7
-ShapeNet [7] 3M+ 7 7 7 3 3 7
-ShapeNetCore [7] 51k+ 7 7 7 3 3 7
-ShapeNetSem [7] 12k 7 7 7 3 3 7
-ModelNet [6] 151k+ 7 7 7 3 3 7
-Table 1 . Summary of datasets with CAD-like data. Note that
-only ABC and CC3D offer CAD models in b-rep (boundary
-representation) format in addition to triangular meshes.
-Fig. 2 . Overview of CC3D autoencoder architecture and
-PVDeConv module. The features from coarse voxel-based
-and fine point-based branches are fused to be unwrapped to
-the output point cloud.
-PVDeConv structure is depicted in Fig. 2. The fine point-
-based branch is implemented as shared transposed MLP, al-
-lowing to maintain the same number of points throughout the
-autoencoder, while the coarse branch allows the features to be
-aggregated at different voxel grid resolutions, thus modelling
-the neighborhood information at different scales.
-The PVDeConv block consists of 3D volumetric deconvo-
-lutions to aggregate the features, dropout, the batch normal-
-ization and the nonlinear activation function after each 3D
-deconvolution. Features from both branches are fused at the
-final level and MLP to produce the output points.
-The transposed 3D convolution operator, used in PVDe-
-Conv, multiplies each input value element-wise by a learnable
-kernel, and sums over the outputs from all input feature chan-
-nels. This operation can be seen as the gradient of 3D convo-
-lution, although it is not an actual deconvolution operation.
-5. EXPERIMENTS
-We evaluate the proposed autoencoder by training first on our
-CC3D dataset, and then on the ShapeNetCore [7] dataset.
-5.1. Training on CC3D
-Dataset. CC3D dataset is randomly split into three non-
-intersecting folds: 80% for training, 10% for testing and 10%
-for validation. Ground-truth point clouds are generated by
-uniformly sampling N= 10 k points on the CAD models sur-
-faces, while the input point clouds are sampled in the same
-manner from corresponding 3D scans of the models. The data
-is normalized to (0, 1).
-Implementation Details. The encoder follows the struc-
-ture in [21], the coarse blocks are ((64, 1, 32), (64, 2, 16),
-(128, 1, 16), 1024), where triplets describe voxel-based con-
-volutional PVConv block in terms of number of channels,
-number of blocks, and voxel resolution. The last number de-
-scribes the resulting embedding size for the coarse part, and
-being combined with shared MLP cloud blocks = (256, 128),
-gives the feature embedding size of 1472. The decoder coarse
-blocks are ((128, 1, 16), (64, 2, 16), (64, 1, 32), 128), whereFig. 3 . Results of our autoencoder on CC3D data with 10k
-points for input and output. The left of each pair of results
-is the input point cloud of 10k, the right is the autoencoder
-reconstruction of 10k points.
-the triplets are PVDeConv concatenated with decoder point-
-based fine blocks of size (256, 128).
-Training setup. The autoencoder is trained with Chamfer
-loss for 50 epochs on two Quadro P6000 with batch size 80 in
-data parallel mode. The overall training takes approximately
-15 hours. The best model is chosen based on the validation
-set.
-Evaluation. The qualitative results of our autoencoder on
-the CC3D data are presented in Fig. 3. We notice that the fine
-details are captured in these challenging cases.
-Method Chamfer distance, 103
-AtlasNet [2] 1.769
-FoldingNet [17] 1.648
-PCN [19] 1.472
-TopNet [3] 0.972
-Ours 0.804
-Table 2 . CC3D autoencoder results on ShapeNetCore
-dataset: comparison against previous works ( N= 2:5k).
-5.2. Training on ShapeNetCore
-To demonstrate the competitive performance of our CC3D
-autoencoder, we train it on the ShapeNetCore dataset follow-
-ing the train/test/val split of [3], with the number of sampled
-point N= 2500 for a fair comparison. Since we do not have
-scanned models for the ShapeNet data, we add a 3% Gaussian
-noise to each point’s location. The rest of the training setup
-is replicated from the CC3D configuration. The final met-
-ric is the mean Chamfer distance averaged per model across
-all classes. The numbers for other methods are reported
-from [3]. The results of the evaluation of our method against
-state-of-the-art methods are shown in Table 2. We note that
-Fig. 4 . Chamfer distance distribution for our autoencoder. On
-test set of CC3D for point clouds of size N= 10 k, mean
-Chamfer distance is 1:26103with standard deviation of
-0:794103. ShapeNetCore test set with N= 2:5k, it is
-0:804103with standard deviation 0:766103.
-Fig. 5 . Results of our autoencoder on ShapeNetCore data.
-The top row is the input 2.5k point clouds, the bottom is the
-reconstruction of our autoencoder.
-our result surpasses the previous works by a significant mar-
-gin. Qualitative examples on ShapeNetCore data are given in
-Fig. 5. The distribution of distances given in Fig. 4 implies
-that CC3D dataset presents advanced challenges for our au-
-toencoder, where it performs at 1:26103average Chamfer
-distance, while it reaches 0:804103on ShapeNetCore.
-6. CONCLUSIONS
-In this work, we proposed a Point-V oxel Deconvolution
-(PVDeConv ) block for a fast and efficient deconvolution
-on 3D point clouds. It was used in combination with a new
-dataset, CC3D, for autoencoding 3D Scans to their corre-
-sponding synthetic CAD models. The CC3D dataset offers
-pairs of CAD models and 3D scans, totaling to 50k+ objects.
-Our CC3D autoencoder on point clouds is memory and time
-efficient. Furthermore, it demonstrates superior results com-
-pared to existing methods on ShapeNet data. As future work,
-different types of losses will be investigated to improve the
-sharpness on edges, such as quadric [5]. Testing the variants
-of CC3D autoencoder with different configurations of stacked
-PVConv and PVDeConv layers will also be considered. Fi-
-nally, we believe that the CC3D dataset itself could assist in
-real 3D scanned data analysis with deep learning methods.7. REFERENCES
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txt/2101.07621.txt

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-arXiv:2101.07621v2  [cs.GT]  29 May 2021Trading Transforms of Non-weighted Simple Games
-and Integer Weights of Weighted Simple Games∗
-Akihiro Kawana†Tomomi Matsui‡
-June 1, 2021
-Abstract
-This study investigates simple games. A fundamental research
-question in this field is to determine necessaryand sufficient condition s
-for a simple game to be a weighted majority game. Taylor and Zwicker
-(1992) showed that a simple game is non-weighted if and only if there
-exists a trading transform of finite size. They also provided an uppe r
-bound on the size of such a trading transform, if it exists. Gvozdev a
-and Slinko (2011) improved that upper bound; their proof employed a
-property of linear inequalities demonstrated by Muroga (1971). In this
-study, we provide a new proof of the existence of a trading transf orm
-when a given simple game is non-weighted. Our proof employs Farkas’
-lemma (1894), and yields an improved upper bound on the size of a
-trading transform.
-We also discuss an integer-weight representation of a weighted sim-
-ple game, improving the bounds obtained by Muroga (1971). We show
-that our bound on the quota is tight when the number of players is
-less than or equal to five, based on the computational results obt ained
-by Kurz (2012).
-Furthermore, we discuss the problem of finding an integer-weight
-representation under the assumption that we have minimal winning
-coalitions and maximal losing coalitions. In particular, we show a
-performance of a rounding method.
-Lastly, we address roughly weighted simple games. Gvozdeva and
-Slinko (2011) showed that a given simple game is not roughly weighted
-if and only if there exists a potent certificate of non-weightedness .
-∗preliminary version of this paper was presented at Seventh I nternational Workshop
-on Computational Social Choice (COMSOC-2018), Rensselaer Polytechnic Institute, Troy,
-NY, USA, 25-27 June, 2018.
-†Graduate School of Engineering, Tokyo Institute of Technol ogy
-‡Graduate School of Engineering, Tokyo Institute of Technol ogy
-1We give an upper bound on the length of a potent certificate of non-
-weightedness. We also discuss an integer-weight representation o f a
-roughly weighted simple game.
-1 Introduction
-A simple game consists of a pair G= (N,W),whereNis a finite set of
-players, and W ⊆2Nis an arbitrary collection of subsets of N. Throughout
-this paper, we denote |N|byn. Usually, the property
-(monotonicity): if S′⊇S∈ W,thenS′∈ W, (1)
-is assumed. Subsets in Ware called winning coalitions . We denote 2N\W
-byL, and subsets in Lare called losing coalitions . A simple game ( N,W)
-is said to be weighted if there exists a weight vector w∈RNandq∈R
-satisfying the following property:
-(weightedness): for any S⊆N,S∈ Wif and only if/summationdisplay
-i∈Swi≥q.(2)
-Previous research established thenecessary andsufficient c onditions that
-guarantee the weightedness of a simple. [Elgot, 1961] and [C how, 1961] in-
-vestigated the theory of threshold logic and showed the cond ition of the
-weightedness in terms of asummability . [Muroga, 1971] proved the suffi-
-ciency of asummability based on the theory of linear inequal ity systems
-and discussed some variations of their results in cases of a f ew variables.
-[Taylor and Zwicker, 1992,Taylor and Zwicker, 1999]obtain ednecessaryand
-sufficient conditions independently in terms of a trading transform . Atrad-
-ing transform ofsizejisacoalition sequence( X1,X2,...,X j;Y1,Y2,...,Y j),
-which may contain repetitions of coalitions, satisfying th e condition ∀p∈N,
-|{i|p∈Xi}|=|{i|p∈Yi}|. A simple game is called k-trade robust if there
-is no trading transform of size jsatisfying 1 ≤j≤k,X1,X2,...,X j∈ W,
-andY1,Y2,...,Y j∈ L. A simple game is called trade robust if it isk-trade
-robust for all positive integers k.
-Taylor and Zwicker showed that a given simple game Gwithnplayers is
-weightedifandonlyif Gis22n-traderobust. In2011, [Gvozdeva and Slinko, 2011]
-showed that agiven simplegame Gis weighted ifandonly if Gis (n+1)nn/2-
-trade robust. [Freixas and Molinero, 2009b] proposed a vari ant of trade ro-
-bustness, called invariant-trade robustness, whichdeter mineswhetherasim-
-ple game is weighted. The relations between the results in th reshold logic
-and simple games are clarified in [Freixas et al., 2016, Freix as et al., 2017].
-2In Section 2, we show that a given simple game Gis weighted if and
-only ifGisαn+1-trade robust, where αn+1denotes the maximal value of
-determinants of ( n+1)×(n+1) 0–1 matrices. It is well-known that αn+1≤
-(n+2)n+2
-2(1/2)(n+1).
-Our definition of a weighted simple game allows for an arbitra ry real
-number of weights. However, any weighted simple game can be r epresented
-by integer weights (e.g., see [Freixas and Molinero, 2009a] ). Aninteger-
-weight representation of a weighted simple game consists of an integer vec-
-torw∈ZNand some q∈Zsatisfying the weightedness property (2).
-[Isbell, 1956] found an example of a weighted simple game wit h 12 players
-withoutauniqueminimum-suminteger-weight representati on. Examplesfor
-9, 10, or11playersaregivenin[Freixas and Molinero, 2009a ,Freixas and Molinero, 2010].
-Inthefieldofthresholdlogic, examples of thresholdfuncti onsrequiringlarge
-weightsarediscussedby[Myhill and Kautz, 1961,Muroga, 19 71,H˚ astad, 1994].
-Some previous studies enumerate (minimal) integer-weight representations
-of simple games with a small number of players (e.g., [Muroga et al., 1962,
-Winder, 1965,Muroga et al., 1970,Krohn and Sudh¨ olter, 199 5]). Inthecase
-ofn= 9 players, refer to [Kurz, 2012]. In general, [Muroga, 1971 ] (Proof of
-Theorem 9.3.2.1) showed that (under the monotonicity prope rty (1) and
-∅ /\e}atio\slash∈ W ∋ N) every weighted simple game has an integer-weight repre-
-sentation satisfying 0 ≤wi≤αn≤(n+ 1)n+1
-2(1/2)n(∀i∈N) and
-0≤q≤nαn≤n(n+1)n+1
-2(1/2)nsimultaneously. Here, αndenotesthemax-
-imal valueof determinantsof n×n0–1matrices. [Wang and Williams, 1991]
-discussed Boolean functions that require more general surf aces to sepa-
-rate their true vectors from false vectors. [Hansen and Podo lskii, 2015] in-
-vestigates the complexity of computing Boolean functions b y polynomial
-threshold functions. [Freixas, 2021] discusses a point-se t-additive pseudo-
-weighting for a simple game, which assigns weights directly to coalitions.
-In Section 3, we slightly improve Muroga’s result and show th at ev-
-ery weighted simple game (satisfying ∅ /\e}atio\slash∈ W ∋ N) has an integer-weight
-representation ( q;w⊤) satisfying |wi| ≤αn(∀i∈N),|q| ≤αn+1, and
-1≤/summationtext
-i∈Nwi≤2αn+1−1 simultaneously. Based on the computational
-results of [Kurz, 2012], we also demonstrate the tightness o f our bound on
-the quota when n≤5.
-For a family of minimal winning coalitions, [Peled and Simeo ne, 1985]
-proposed a polynomial-time algorithm for checking the weig htedness of a
-given simple game. They also showed that for weighted simple games repre-
-sented by minimal winning coalitions, all maximal losing co alitions can be
-computed in polynomial time. When we have minimal winning co alitions
-3and maximal losing coalitions, there exists a linear inequa lity system whose
-solution gives a weight vector w∈RNandq∈Rsatisfying property (2).
-However, it isless straightforward tofindaninteger-weigh t representation as
-the problem transforms from linear programming to integer p rogramming.
-In Section 4, we address the problem of finding an integer-wei ght rep-
-resentation under the assumption that we have minimal winni ng coalitions
-and maximal losing coalitions. We show that an integer-weig ht represen-
-tation is obtained by carefully rounding a solution of the li near inequality
-system multiplied by at most (2 −√
-2)n+(√
-2−1).
-A simple game G= (N,W) is called roughly weighted if there exist a
-non-negative vector w∈RN
-+and a real number q∈R, not all equal to
-zero ((q;w⊤)/\e}atio\slash=0⊤), such that for any S⊆Ncondition/summationtext
-i∈Swi< qim-
-pliesS/\e}atio\slash∈ W, and/summationtext
-i∈Swi> qimpliesS∈ W. We say that ( q;w⊤) is a
-rough voting representation forG. Roughly weighted simple games were ini-
-tially introduced by [Baugh, 1970]. [Muroga, 1971] (p. 208) studied them
-under the name of pseudothreshold functions. [Taylor and Zw icker, 1999]
-discussed roughly weighted simple games and constructed se veral examples.
-[Gvozdeva and Slinko, 2011] developed a theory of roughly we ighted simple
-games. A trading transform ( X1,X2,...,X j;Y1,Y2,...,Y j) with all coali-
-tionsX1,X2,...,X jwinningand Y1,Y2,...,Y jlosingiscalled a certificate of
-non-weightedness . This certificate is said to be potentif the grand coalition
-Nis among X1,X2,...,X jand the empty coalition is among Y1,Y2,...,Y j.
-[Gvozdeva and Slinko, 2011] showed that under the the monoto nicity prop-
-erty (1) and ∅ /\e}atio\slash∈ W ∋ N, a given simple game Gis not roughly weighted if
-and only if thereexists a potent certificate of non-weighted ness whose length
-islessthanorequalto( n+1)nn/2. Furtherresearchonroughlyweightedsim-
-plegamesappearsin[Gvozdeva et al., 2013,Freixas and Kurz , 2014,Hameed and Slinko, 2015].
-In Section 5, we show that (under the the monotonicity proper ty (1) and
-∅ /\e}atio\slash∈ W ∋ N) the length of a potent certificate of non-weightedness is le ss
-than or equal to 2 αn+1, if it exists. We also show that a roughly weighted
-simple game (satisfying ∅ /\e}atio\slash∈ W ∋ N) has an integer vector ( q;w⊤) of rough
-voting representation satisfying 0 ≤wi≤αn−1(∀i∈N), 0≤q≤αnand
-0≤/summationtext
-i∈Nwi≤2αn.
-2 TradingTransformsof Non-weighted Simple Games
-In this section, we discuss the size of a trading transform th at guarantees
-the non-weightedness of a given simple game. Throughout thi s section, we
-do not need to assume the monotonicity property (1). First, w e introduce a
-4linear inequality system for determining the weightedness of a given simple
-game. For any nonempty family of player subsets �� /\e}atio\slash=N ⊆2N, we introduce
-a 0–1 matrix A(N) = (a(N)Si) whose rows are indexed by subsets in Nand
-columns are indexed by players in Ndefined by
-a(N)Si=/braceleftbigg1 (ifi∈S∈ N),
-0 (otherwise) .
-A given simple game G= (N,W) is weighted if and only if the following
-linear inequality system is feasible:
-P1:/parenleftbiggA(W)1 0
-−A(L)−1−1/parenrightbigg
-w
-−q
-ε
-≥0,
-ε >0,
-where0(1) denotes a zero vector (all-one vector) of an appropriate di men-
-sion.
-Farkas’ Lemma [Farkas, 1902] states that P1 is infeasible if and only if
-the following system is feasible:
-D1:
-A(W)⊤−A(L)⊤
-1⊤−1⊤
-0⊤−1⊤
-/parenleftbiggx
-y/parenrightbigg
-=
-0
-0
-−1
-,
-x≥0,y≥0.
-For simplicity, we denote D1 by A1z=c,z≥0,where
-A1=
-A(W)⊤−A(L)⊤
-1⊤−1⊤
-0⊤−1⊤
-,z=/parenleftbiggx
-y/parenrightbigg
-,andc=
-0
-0
-−1
-.
-Subsequently, we assume that D1 is feasible. Let /tildewiderA1z=/tildewidecbe a linear
-equality system obtained from A1z=cby repeatedly removing redundant
-equalities. A column submatrix /hatwideBof/tildewiderA1is called a basis matrix if/hatwideBis a
-square invertible matrix. Variables corresponding to the c olumns of/hatwideBare
-calledbasic variables , andJ/hatwideBdenotes an index set of basic variables. A
-basic solution with respect to /hatwideBis a vector zdefined by
-zi=/braceleftbigg/hatwidezi(i∈J/hatwideB),
-0 (i/\e}atio\slash∈J/hatwideB),
-5where/hatwidezis a vector of basic variables satisfying /hatwidez=/hatwideB−1/tildewidec. It is well-known
-that if a linear inequality system D1 is feasible, then it has a basic feasible
-solution.
-Letz′be a basic feasible solution of D1 with respect to a basis matr ixB.
-By Cramer’s rule, z′
-i= det(Bi)/det(B) for each i∈JB,whereBiis a matrix
-formed by replacing i-th column of Bby/tildewidec. Because Biis an integer matrix,
-det(B)z′
-i= det(Bi) is an integer for any i∈JB. Let (x′⊤,y′⊤)⊤be a vector
-corresponding to z′,and (x∗⊤,y∗⊤) =|det(B)|(x′⊤,y′⊤). Cramer’s rule
-states that both x∗andy∗are integer vectors. The pair of vectors x∗and
-y∗satisfies the following conditions:
-A(W)⊤x∗−A(L)⊤y∗=|det(B)|(A(W)⊤x′−A(L)⊤y′) =|det(B)|0=0,/summationdisplay
-S∈Wx∗
-S−/summationdisplay
-S∈Ly∗
-S=|det(B)|(1⊤x′−1⊤y′) =|det(B)|0 = 0,
-/summationdisplay
-S∈Ly∗
-S=|det(B)|1⊤y′=|det(B)|,
-x∗=|det(B)|x′≥0,andy∗=|det(B)|y′≥0.
-Next, we construct a trading transform corresponding to the pair ofx∗and
-y∗. LetX= (X1,X2,...,X|det(B)|) be a sequence of winning coalitions,
-where each winning coalition S∈ Wappears in Xx∗
-S-times. Similarly, we
-introduce a sequence Y= (Y1,Y2,...,Y|det(B)|),where each losing coalition
-S∈ Lappears in Yy∗
-S-times. The above equalities imply that ( X;Y) is a
-trading transform of size |det(B)|. Therefore, we have shown that if D1 is
-feasible, then a given simple game G= (N,W) is not|det(B)|-trade robust.
-Finally, weprovidean upperboundon |det(B)|. Letαnbethemaximum
-of the determinants of n×n0–1 matrices. For any n×n0–1 matrix M,it is
-easy to show that det( M)≥ −αnby swapping two rows of M(whenn≥2).
-If a column of Bis indexed by a component of x(i.e., indexed by a winning
-coalition), then each component of the column is either 0 or 1 . Otherwise,
-a column (of B) is indexed by a component of y(i.e., indexed by a losing
-coalition) whose components are either 0 or −1. Now, we apply elementary
-matrix operations to B(see Figure 1). For each column of Bindexed by
-a component of y, we multiply the column by ( −1). The resulting matrix,
-denoted by B′, is a 0–1 matrix satisfying |det(B)|=|det(B′)|.
-AsBis a submatrix of A1, the number of rows (columns) of B, denoted
-byn′, is less than or equal to n+ 2. When n′< n+ 2, we obtain the
-desired result: |det(B)|=|det(B′)| ≤αn′≤αn+1. Ifn′=n+ 2, then B
-has a row vector corresponding to equality 1⊤x−1⊤y= 0, which satisfies
-the condition that each component is either 1 or −1, and thus B′has an
-60 0 1 1 0−1
-0 1 0 1 0 0
-1 0 0 1 0−1
-1 1 1 0 −1−1
-1 1 1 1 −1−1
-0 0 0 0 −1−1
-B0 0 1 1 0 1
-0 1 0 1 0 0
-1 0 0 1 0 1
-1 1 1 0 1 1
-1 1 1 1 1 1
-0 0 0 0 1 1
-B′
-Figure 1: Example of elementary matrix operations for D1.
-all-one row vector. Lemma 2.1 (c1) appearing below states th at|det(B)|=
-|det(B′)| ≤αn′−1≤αn+1.
-Lemma 2.1. LetMbe ann×n0–1 matrix, where n≥2.
-(c1)If a row (column) vector of Mis the all-one vector, then |det(M)| ≤αn−1.
-(c2)If a row (column) vector of Mis a 0–1 vector consisting of a unique
-0-component and n−11-components, then |det(M)| ≤2αn−1.
-Proof of (c1). Assume that the first column of Mis the all-one vector. We
-apply the following elementary matrix operations to M(see Figure 2). For
-each column of Mexcept the first column, if the first component is equal to
-1, then we multiply the column by ( −1) and add the all-one column vector.
-The obtained matrix, denoted by M′, is ann×n0–1 matrix satisfying
-|det(M)|=|det(M′)|,and the first row is a unit vector. Thus, it is obvious
-that|det(M′)| ≤αn−1.
-11 0 1 0
-11 1 1 0
-10 1 0 0
-11 1 0 1
-10 0 1 1
-M10 0 0 0
-10 1 0 0
-11 1 1 0
-10 1 1 1
-11 0 0 1
-M′
-Figure 2: Example of elementary matrix operations for (c1).
-Proof of (c2). Assume that the first column vector of M, denoted by a,
-contains exactly one 0-component. Obviously, e=1−ais a unit vector.
-LetM1andMebe a pair of matrices obtained from Mwith the first column
-7replaced by 1ande, respectively. Then, it is easy to prove that
-|det(M)|=|det(M1)−det(Me)| ≤ |det(M1)|+|det(Me)| ≤2αn−1.
-QED
-From the above discussion, we obtain the following theorem ( without
-the assumption of the monotonicity property (1)).
-Theorem 2.2. A given simple game G= (N,W)withnplayers is weighted
-if and only if Gisαn+1-trade robust, where αn+1is the maximum of deter-
-minants of (n+1)×(n+1)0–1 matrices.
-Proof. If a given simple game is not αn+1-trade robust, then it is not trade
-robust and, thus, not weighted, as shown by [Taylor and Zwick er, 1992,
-Taylor and Zwicker, 1999]. We have discussed the inverse imp lication: if
-a given simple game Gis not weighted, then the linear inequality system P1
-is infeasible. Farkas’ lemma [Farkas, 1902] implies that D1 is feasible. From
-the above discussion, we have a trading transform ( X1,...,X j;Y1,...Yj)
-satisfying j≤αn+1,X1,...,X j∈ W, andY1,...,Y j∈ L. QED
-Applying the Hadamard’s evaluation [Hadamard, 1893] of the determi-
-nant, we obtain Theorem 2.3.
-Theorem 2.3. For any positive integer n,αn≤(n+1)n+1
-2(1/2)n.
-The exact values of αnfor small positive integers nappear in “The On-
-LineEncyclopediaof Integer Sequences (A003432)” [Sloane et al., 2018] and
-Table 1.
-3 Integer Weights of Weighted Simple Games
-This section reviews the integer-weight representations o f weighted simple
-games. Throughoutthis section, we donot need to assume the m onotonicity
-property (1), except in Table 1.
-Theorem 3.1. Assume that a given simple game G= (N,W)satisfies
-∅ /\e}atio\slash∈ W ∋ N. If a given simple game Gis weighted, then there exists an
-integer-weight representation (q;w⊤)ofGsatisfying |wi| ≤αn(∀i∈N),
-|q| ≤αn+1, and1≤/summationtext
-i∈Nwi≤2αn+1−1.
-8Proof. It is easy to show that a given simple game G= (N,W) is weighted
-if and only if the following linear inequality system is feas ible:
-P2:A(W)w≥q1,
-A(L)w≤q1−1,
-1⊤w≤u−1.
-We define
-A2=
-A(W)10
-−A(L)−10
-−1⊤0 1
-,v=
-w
-−q
-u
-,d=
-0
-1
-1
-,
-and denote the inequality system P2 by A2v≥d.
-Subsequently, we assume that P2 is feasible. A non-singular submatrix
-/hatwideBofA2is called a basis matrix . Variables corresponding to columns of /hatwideB
-are called basic variables , andJ/hatwideBdenotes an index set of basic variables.
-Letd/hatwideBbe a subvector of dcorresponding to rows of /hatwideB. Abasic solution
-with respect to /hatwideBis a vector vdefined by
-vi=/braceleftbigg/hatwidevi(i∈J/hatwideB),
-0 (i/\e}atio\slash∈J/hatwideB),
-where/hatwidevis a vector of basic variables satisfying /hatwidev=/hatwideB−1d/hatwideB. It is well-known
-that if a linear inequality system P2 is feasible, there exis ts a basic feasible
-solution.
-Let (w′⊤,−q′,u′)⊤be a basic feasible solution of P2 with respect to a
-basis matrix B. Assumption ∅ /\e}atio\slash∈ Wimplies that 0 ≤q′−1 and, thus,
-−q′/\e}atio\slash= 0. As N∈ W, we have inequalities u′−1≥1⊤w′≥q′≥1,which
-imply that u′/\e}atio\slash= 0. The definition of a basic solution implies that −qand
-uare basic variables with respect to the basis matrix B. Thus, Bhas
-columns corresponding to basic variables −qandu. A column of Bindexed
-byuis called the last column. As Bis invertible, the last column of Bis
-not the zero vector, and thus Bincludes a row corresponding to inequality
-1⊤w≤u−1, which is called the last row (see Figure 3). Here, the numbe r
-of rows (columns) of B, denoted by n′, is less than or equal to n+2.
-For simplicity, we denote the basic feasible solution ( w′⊤,−q′,u′)⊤by
-v′. By Cramer’s rule, v′
-i= det(Bi)/det(B) for each i∈JB,whereBiis
-obtained from Bwith a column correspondingto variable vireplaced by dB.
-Because Biis an integer matrix, det( B)v′
-i= det(Bi) is an integer for any
-9i∈JB. Cramer’s rule states that ( w∗⊤,−q∗,u∗) =|det(B)|(w′⊤,−q′,u′) is
-an integer vector satisfying the following conditions:
-A(W)w∗=|det(B)|A(W)w′≥ |det(B)|q′1=q∗1,
-A(L)w∗=|det(B)|A(L)w′≤ |det(B)|(q′1−1)≤q∗1−1,and
-1⊤w∗=|det(B)|1⊤w′≤ |det(B)|(u′−1)≤u∗−1.
-From the above, ( q∗;w∗⊤) is an integer-weight representation of G. As
-N∈ W, we obtain 1⊤w∗≥q∗=|det(B)|q′≥1.
-w1w2w3w4−q u
-1 1 1 0 10
-0 1 1 1 10
-0−1−1 0 −10
-−1 0 0 −1−10
-0−1 0 −1−10
-−1−1−1−101
-B
-w1w2w3w4−q u
-1 1 1 0 00
-0 1 1 1 00
-0−1−1 0 10
-−1 0 0 −110
-0−1 0 −110
-−1−1−1−111
-Bqw1w2w3w4−q
-1 1 1 0 0
-0 1 1 1 0
-0−1−1 0 1
-−1 0 0 −11
-0−1 0 −11
-B′
-qw1w2w3w4−q
-1 1 1 0 0
-0 1 1 1 0
-0 1 1 0 1
-1 0 0 1 1
-0 1 0 1 1
-B′′
-q
-w1w2w3w4−q u
-101 0 10
-001 1 10
-01−1 0 −10
-−110−1−10
-010−1−10
-−11−1−101
-B2w1w2w3w4−q
-101 0 1
-001 1 1
-01−1 0 −1
-−110−1−1
-010−1−1
-B′
-2w1w2w3w4−q
-101 0 1
-001 1 1
-011 0 1
-110 1 1
-010 1 1
-B′′
-2
-w1w2w3w4−q u
-1 1 1 0 10
-0 1 1 1 10
-0−1−1 0 −11
-−1 0 0 −1−11
-0−1 0 −1−11
-−1−1−1−101
-Buw1w2w3w4−q u
-1 1 1 0 10
-0 1 1 1 10
-0 1 1 0 11
-1 0 0 1 11
-0 1 0 1 11
-1 1 1 1 01
-B′
-u
-Figure 3: Examples of elementary matrix operations for P2.
-Now, we discuss the magnitude of |q∗|=|det(Bq)|,whereBqis obtained
-10fromBwith a column corresponding to variable −qreplaced by dB. As the
-last column of Bqis a unit vector, we delete the last column and the last row
-fromBqand obtain a matrix B′
-qsatisfying det( Bq) = det(B′
-q). We apply
-the following elementary matrix operations to B′
-q. First, we multiply the
-column corresponding to variable −q(which is equal to dB) by (−1). Next,
-we multiply the rows indexed by losing coalitions by ( −1). The resulting
-matrix, denoted by B′′
-q, is 0–1 valued and satisfies the following condition:
-|q∗|=|det(Bq)|=|det(B′
-q)|=|det(B′′
-q)| ≤αn′−1≤αn+1.
-Next, we show that |w∗
-i| ≤αn(i∈N). Ifw∗
-i/\e}atio\slash= 0, then wiis a basic
-variable that satisfies |w∗
-i|=|det(Bi)|,whereBiis obtained from Bbut
-the column corresponding to variable wiis replaced by dB. In a manner
-similar to that above, we delete the last column and the last r ow from Bi
-and obtain a matrix B′
-isatisfying det( Bi) = det(B′
-i). Next, we multiply a
-column corresponding to variable wiby (−1). We multiply rows indexed by
-losing coalitions by ( −1) and obtain a 0–1 matrix B′′
-i. Matrix Bicontains
-a column corresponding to the original variable −q, which contains values 1
-or−1. Thus, matrix B′′
-icontains a column vector that is equal to an all-one
-vector. Lemma 2.1 (c1) implies that
-|w∗
-i|=|det(Bi)|=|det(B′
-i)|=|det(B′′
-i)| ≤αn′−2≤αn.
-Lastly, we discuss the value of |u∗|=|det(Bu)|,whereBuis obtained
-fromBbut the last column (column indexed by variable u) is replaced by
-dB. In a manner similar to that above, we multiply the last colum n by
-(−1), multiply the rows indexed by losing coalitions by ( −1), and multiply
-the last row by ( −1). The resulting matrix, denoted by B′
-u, is a 0–1 matrix
-in which the last row contains exactly one 0-component (inde xed by variable
-−q). Lemma 2.1 (c2) implies that
-|u∗|=|det(Bu)|=|det(B′
-u)| ≤2αn′−1≤2αn+1,
-and thus 1⊤w∗≤u∗−1≤ |u∗|−1≤2αn+1−1. QED
-[Kurz, 2012] exhaustively generated all weighted voting ga mes satisfying
-the monotonicity property (1) for up to nine voters. Table 1 s hows max-
-ima of the exact values of minimal integer-weight represent ations obtained
-by [Kurz, 2012], Muroga’s boundsin [Muroga, 1971], and our u pperbounds.
-The table shows that our bound on the quota is tight when n≤5.
-11Table 1: Exact values of integer weights representations.
-n 1 2 3 4 5 6 7 8 9 10 11
-αn† 1 1 2 3 5 9 32 56 144 320 1458
-max
-(N,W)min
-[q;w]max
-iwi‡1 1 2 3 5 9 18 42 110
-Muroga’s bound (αn)•1 1 2 3 5 9 32 56 144 320 1458
-max
-(N,W)min
-[q;w]q‡1 2 3 5 9 18 40 105 295
-Our bound (αn+1)1 2 3 5 9 32 56 144 320 1458
-Muroga’s bound (nαn)•1 2 6 12 25 54 224 448 1296 3200 16038
-max
-(N,W)min
-[q;w]/summationtext
-iwi‡1 2 4 8 15 33 77 202 568
-Our bound (2αn+1−1)1 3 5 9 17 63 111 287 639 2915
-†[Sloane et al., 2018], ‡[Kurz, 2012], •[Muroga, 1971].
-4 Rounding Method
-This section addresses the problem of findinginteger-weigh t representations.
-In this section, we assume the monotonicity property (1). In addition, a
-weighted simple game is given by a triplet ( N,Wm,LM),whereWmand
-LMdenote the set of minimal winning coalitions and the set of ma ximal
-losing coalitions, respectively. We also assume that the em pty set is a losing
-coalition, Nis a winning coalition, and every player in Nis not a null
-player. Thus, there exists an integer-weight representati on in which q≥1
-andwi≥1 (∀i∈N).
-We discuss a problem for findingan integer-weight represent ation, which
-is formulated by the following integer programming problem :
-Q: find a vector ( q;w)
-satisfying/summationdisplay
-i∈Swi≥q(∀S∈ Wm), (3)
-/summationdisplay
-i∈Swi≤q−1 (∀S∈ LM), (4)
-q≥1, wi≥1 (∀i∈N), (5)
-q∈Z, wi∈Z(∀i∈N). (6)
-A linear relaxation problem Q is obtained from Q by dropping the integer
-constraints (6).
-Let (q∗;w∗⊤) be a basic feasible solution of the linear inequality sys-
-temQ. Our proof in the previous section showed that |det(B∗)|(q∗;w∗⊤)
-12gives a solution of Q (i.e., an integer-weight representati on), where B∗de-
-notes a corresponding basis matrix of Q. When |det(B∗)|> n, there ex-
-ists a simple method for generating a smaller integer-weigh t representation.
-For any weight vector w= (w1,w2,...,w n)⊤, we denote the integer vector
-(⌊w1⌋,⌊w2⌋,...,⌊wn⌋)⊤by⌊w⌋. Given a solution ( q∗;w∗⊤) ofQ, we intro-
-duce an integer vector w′=⌊nw∗⌋and an integer q′=⌊n(q∗−1)⌋+1. For
-any minimal winning coalition S∈ Wm, we have that
-/summationdisplay
-i∈Sw′
-i>/summationdisplay
-i∈S(nw∗
-i−1)≥n/summationdisplay
-i∈Sw∗
-i−n≥nq∗−n=n(q∗−1)≥ ⌊n(q∗−1)⌋,
-/summationdisplay
-i∈Sw′
-i≥ ⌊n(q∗−1)⌋+1 =q′.
-Each maximal losing coalition S∈ LMsatisfies
-/summationdisplay
-i∈Sw′
-i≤/summationdisplay
-i∈Snw∗
-i≤n(q∗−1),
-/summationdisplay
-i∈Sw′
-i≤ ⌊n(q∗−1)⌋=q′−1.
-Thus, the pair of w′andq′gives an integer-weight representation satisfying
-(q′;w′⊤)≤n(q∗;w∗⊤). In the remainder of this section, we show that there
-exists an integer-weight representation (vector) that is l ess than or equal
-to ((2−√
-2)n+(√
-2−1))(q∗;w∗⊤)<(0.5858n+0.4143)(q∗;w∗⊤) for any
-solution ( q∗;w∗⊤) ofQ.
-Theorem 4.1. Let(q∗;w∗⊤)be a solution of Q. We define ℓ1= (2−√
-2)n−
-(√
-2−1)andu1= (2−√
-2)n+(√
-2−1). Then, there exists a real number
-λ•∈[ℓ1,u1]so that the pair Q=⌊λ•(q∗−1)⌋+1andW=⌊λ•w∗⌋gives a
-feasible solution of Q (i.e., an integer-weight representa tion).
-Proof. For any positive real λ, it is easy to see that each maximal losing
-coalition S∈ LMsatisfies
-/summationdisplay
-i∈S⌊λw∗
-i⌋ ≤/summationdisplay
-i∈Sλw∗
-i≤λ(q∗−1),
-/summationdisplay
-i∈S⌊λw∗
-i⌋ ≤ ⌊λ(q∗−1)⌋.
-To discuss the weights of minimal winning coalitions, we int roduce a
-function g(λ) =λ−/summationtext
-i∈N(λw∗
-i−⌊λw∗
-i⌋). In thesecond part of this proof, we
-show that if we choose Λ ∈[ℓ1,u1] uniformly at random, then E[ g(Λ)]≥0.
-13This implies that ∃λ•∈[ℓ1,u1] satisfying g(λ•)>0, because g(λ) is right-
-continuous, piecewise linear, and not a constant function. Wheng(λ•)>0,
-each minimal winning coalition S∈ Wmsatisfies
-λ•>/summationdisplay
-i∈N(λ•w∗
-i−⌊λ•w∗
-i⌋)≥/summationdisplay
-i∈S(λ•w∗
-i−⌊λ•w∗
-i⌋) =/summationdisplay
-i∈Sλ•w∗
-i−/summationdisplay
-i∈S⌊λ•w∗
-i⌋,
-(7)
-which implies
-/summationdisplay
-i∈S⌊λ•w∗
-i⌋>/summationdisplay
-i∈Sλ•w∗
-i−λ•=λ•/parenleftBigg/summationdisplay
-i∈Sw∗
-i−1/parenrightBigg
-≥λ•(q∗−1)≥ ⌊λ•(q∗−1)⌋,
-and thus /summationdisplay
-i∈S⌊λ•w∗
-i⌋ ≥ ⌊λ•(q∗−1)⌋+1.
-Finally, we show that E[ g(Λ)]≥0 if we choose Λ ∈[ℓ1,u1] uniformly at
-random. It is obvious that
-E[g(Λ)] = E[Λ] −/summationdisplay
-i∈NE[(Λw∗
-i−⌊Λw∗
-i⌋)] =ℓ1+u1
-2−/summationdisplay
-i∈N/integraldisplayu1
-ℓ1(λw∗
-i−⌊λw∗
-i⌋)dλ
-u1−ℓ1
-= (2−√
-2)n−/summationdisplay
-i∈N/integraldisplayu1
-ℓ1(λw∗
-i−⌊λw∗
-i⌋)dλ
-u1−ℓ1.
-Let us discuss the last term appearing above. By substitutin gµforλw∗
-i, we
-obtain
-/integraldisplayu1
-ℓ1(λw∗
-i−⌊λw∗
-i⌋)dλ
-u1−ℓ1=/integraldisplayu1w∗
-i
-ℓ1w∗
-i(µ−⌊µ⌋)dµ
-w∗
-i(u1−ℓ1)
-≤/integraldisplay0
-−w∗
-i(u1−ℓ1)(µ−⌊µ⌋)dµ
-w∗
-i(u1−ℓ1)=/integraldisplay0
-−x(µ−⌊µ⌋)dµ
-x,
-where the last equality is obtained by setting x=w∗
-i(u1−ℓ1). Asu1−ℓ1=
-2(√
-2−1) andw∗
-i≥1, it is clear that x=w∗
-i(u1−ℓ1)≥2(√
-2−1). Here,
-we introduce a function f(x) =/integraldisplay0
-−x(µ−⌊µ⌋)dµ
-x. According to numerical
-14calculations (see Figure 4), inequality x≥2(√
-2−1) implies that f(x)≤
-2−√
-2.
-0 1 2 3 4 5
-x0.450.50.550.60.650.70.750.8f(x)
-Figure 4: Plot of function f(x) =/integraldisplay0
-−x(µ−⌊µ⌋)dµ
-x.
-From the above, we obtain the desired result
-E[g(Λ)]≥(2−√
-2)n−/summationdisplay
-i∈N(2−√
-2) = (2−√
-2)n−(2−√
-2)n= 0.
-QED
-5 Roughly Weighted Simple Games
-In this section, we discuss roughly weighted simple games. F irst, we show
-an upper bound of the length of a potent certificate of non-wei ghtedness.
-Theorem 5.1. Assume that a given simple game G= (N,W)satisfies ∅ /\e}atio\slash∈
-W ∋Nand the monotonicity property (1). If a given simple game Gis not
-roughly weighted, then there exists a potent certificate of n on-weightedness
-whose length is less than or equal to 2αn+1.
-Proof. Let us introduce a linear inequality system:
-P3:/parenleftbiggA(W)1
-−A(L)−1/parenrightbigg/parenleftbiggw
-−q/parenrightbigg
-≥0,
-1⊤w>0.
-15First, we show that if P3 is feasible, then a given simple game is roughly
-weighted. Let ( q′;w′⊤) be a feasible solution of P3. We introduce a new
-voting weight w′′
-i= max{w′
-i,0}for each i∈N. We show that ( q′;w′′⊤) is a
-rough voting representation. As 1⊤w′>0, vector w′includes at least one
-positivecomponent,andthus w′′/\e}atio\slash=0. Ifacoalition Ssatisfies/summationtext
-i∈Sw′′
-i< q′,
-thenq′>/summationtext
-i∈Sw′′
-i≥/summationtext
-i∈Sw′
-i,and thus Sis losing. Consider the case in
-which a coalition Ssatisfies/summationtext
-i∈Sw′′
-i> q′. LetS′={i∈S|w′
-i>0}. It is
-obvious that q′</summationtext
-i∈Sw′′
-i=/summationtext
-i∈S′w′′
-i=/summationtext
-i∈S′w′
-iand thus S′is winning.
-The monotonicity property (1) and S′⊆Simply that Sis winning.
-From the above discussion, it is obvious that if a given simpl e game is
-not roughly weighted, then P3 is infeasible. Farkas’ Lemma [ Farkas, 1902]
-states that
-D3:/parenleftbiggA(W)⊤−A(L)⊤
-1⊤−1⊤/parenrightbigg/parenleftbiggx
-y/parenrightbigg
-=/parenleftbigg−1
-0/parenrightbigg
-,
-x≥0,y≥0,
-is feasible if and only if P3 is infeasible. By introducing an artificial non-
-negative variable u≥0 and equality 1⊤x=u−1, we obtain a linear
-inequality system:
-D3+:
-A(W)⊤−A(L)⊤0
-1⊤−1⊤0
-1⊤0⊤−1
-
-x
-y
-u
-=
-−1
-0
-−1
-,
-x≥0,y≥0, u≥0.
-It is obvious that D3 is feasible if and only if D3+is feasible.
-Next, we construct a trading transform from a basic feasible solution
-of D3+. Let/tildewiderA3z=/tildewidec′be a linear equality system obtained from D3+by
-repeatedly removing redundant equalities. As D3+is feasible, there exists a
-basic feasible solution, denoted by z′, and a corresponding basis matrix B
-of/tildewiderA3. From Cramer’s rule, z′
-S/\e}atio\slash= 0 implies that z′
-S= det(BS)/det(B) for
-eachS⊆N,whereBSis obtained from Bwith a column corresponding to
-variable zSreplaced by/tildewidec′. Obviously, |det(B)|z′is a non-negative integer
-vector. We denote by ( x′⊤,y′⊤,u′)⊤the basic feasible solution z′. We recall
-that∅ /\e}atio\slash∈ W ∋ Nandintroduceapairofnon-negative integer vectors ( x∗,y∗)
-defined as follows:
-x∗
-S=/braceleftbigg|det(B)|x′
-S (ifS∈ W \{N}),
-|det(B)|(x′
-N+1) (if S=N),
-y∗
-S=/braceleftbigg|det(B)|y′
-S (ifS∈ N \{∅} ),
-|det(B)|(y′
-∅+1) (if S=∅).
-16Subsequently, χ(S) denotes the characteristic vector of a coalition S. It is
-easy to see that pair ( x∗,y∗) satisfies
-A(W)⊤x∗−A(L)⊤y∗=/summationdisplay
-S∈Wχ(S)x∗
-S−/summationdisplay
-S∈Lχ(S)y∗
-S
-=/summationdisplay
-S∈Wχ(S)|det(B)|x′
-S+χ(N)|det(B)|−/summationdisplay
-S∈Lχ(S)|det(B)|y′
-S−χ(∅)|det(B)|
-=|det(B)|/parenleftBigg/parenleftBigg/summationdisplay
-S∈Wχ(S)x′
-S−/summationdisplay
-S∈Lχ(S)y′
-S/parenrightBigg
-+χ(N)−χ(∅)/parenrightBigg
-=|det(B)|/parenleftBig/parenleftBig
-A(W)⊤x′−A(L)⊤y′/parenrightBig
-+1−0/parenrightBig
-=|det(B)|(−1+1−0) =0
-and
-/summationdisplay
-S∈Wx∗
-S−/summationdisplay
-S∈Ly∗
-S=/summationdisplay
-S∈W|detB|x′
-S+|det(B)|−/summationdisplay
-S∈L|det(B)|y′
-S−|det(B)|
-=|det(B)|/parenleftBigg/parenleftBigg/summationdisplay
-S∈Wx′
-S−/summationdisplay
-S∈Ly′
-S/parenrightBigg
-+1−1/parenrightBigg
-=|det(B)|(0+1−1) = 0.
-Next, we can construct a trading transform ( X;Y) corresponding to the
-pair ofx∗andy∗by analogy with the proof of Theorem 2.2. Both x∗
-Nand
-y∗
-∅are positive and ∅ /\e}atio\slash∈ W ∋ N; therefore ( X;Y) is a potent certificate of
-non-weightedness.
-Lastly, we discuss the length of ( X;Y). The number of rows (columns)
-ofB, denoted by n′, is less than or equal to n+2. The basic feasible solution
-z′satisfies that u′= 1+1⊤x′≥1>0, and thus Cramer’s rule states that
-det(B)u′= det(Bu) (Figure 5 shows an example). We multiply columns
-ofBucorresponding to components in ( y⊤,u) by (−1) and obtain a 0–1
-matrixB′
-usatisfying |det(Bu)|=|det(B′
-u)|. As/tildewidec′includes at most one 0-
-component, Lemma 2.1 implies that |det(B′
-u)| ≤2αn′−1≤2αn+1. Thus, the
-length of ( X;Y) satisfies
-/summationdisplay
-S∈Wx∗
-S=/summationdisplay
-S∈W|det(B)|x′
-S+|det(B)|=|det(B)|(1⊤x′+1)
-=|det(B)|(u′−1+1) = |det(B)|u′=|det(B)u′|
-=|det(Bu)|=|det(B′
-u)| ≤2αn+1.
-QED
-In the rest of this section, we discuss integer voting weight s and a quota
-of a rough voting representation. We say that a player i∈Nis apasserif
-and only if every coalition S∋iis winning.
-17u
-0 0 1 1 0−10
-0 1 0 1 0 0 0
-1 0 0 1 0−10
-1 1 1 0 −1−10
-1 1 1 1 −1−10
-1 1 1 1 0 0−1
-B
-/tildewidec′
-0 0 1 1 0−1−1
-0 1 0 1 0 0−1
-1 0 0 1 0−1−1
-1 1 1 0 −1−1−1
-1 1 1 1 −1−10
-1 1 1 1 0 0−1
-Bu0 0 1 1 0 1 1
-0 1 0 1 0 0 1
-1 0 0 1 0 1 1
-1 1 1 0 1 1 1
-1 1 1 1 1 1 0
-1 1 1 1 0 0 1
-B′
-u
-Figure 5: Example of elementary matrix operations for D3+.
-Theorem 5.2. Assume that a given simple game G= (N,W)satisfies
-∅ /\e}atio\slash∈ W ∋ N.If a given simple game Gis roughly weighted, then there exists
-an integer vector (q;w⊤)of the rough voting representation satisfying 0≤
-wi≤αn−1(∀i∈N),0≤q≤αn, and1≤/summationtext
-i∈Nwi≤2αn.
-Proof. First, we show that if a given game is roughly weighted , then either
-P4:
-A(W)0
-−A(L)0
-−1⊤1
-/parenleftbiggw
-u/parenrightbigg
-≥
-1
-−1
-0
-,w≥0,u≥0,
-is feasible or there exists at least one passer. Suppose that a given simple
-game has a rough voting representation ( q;w⊤). Ifq >0, then (1 /q)w
-becomes a feasible solution of P4 by setting uto a sufficiently large positive
-number. Consider the case q≤0. Assumption ∅ /\e}atio\slash∈ Wimplies that 0 ≤q,
-and thus we obtain q= 0. Properties ( q,w⊤)/\e}atio\slash=0⊤andw≥0imply that
-∃i◦∈N,wi◦>0, i.e., a given game Ghas a passer i◦.
-When a given game Ghas a passer i◦∈N, then there exists a rough
-18voting representation ( q◦;w◦⊤) defined by
-w◦
-i=/braceleftbigg1 (i=i◦),
-0 (i/\e}atio\slash=i◦),q◦= 0,
-which produces the desired result.
-Lastly, we consider the case in which P4 is feasible. It is wel l-known that
-when P4 is feasible, there exists a basic feasible solution. Let (w′⊤,u′)⊤be
-a basic feasible solution of P4 and Bbe a corresponding basis matrix. It is
-easy to see that (1; w′⊤) is a rough voting representation of G. Assumption
-N∈ Wimplies the positivity of u′becauseu′≥1⊤w′≥1. Then, variable
-uis a basic variable, and thus Bincludes a column corresponding to u,
-which is called the last column. The non-singularity of Bimplies that a
-column corresponding to uis not the zero vector, and thus Bincludes a row
-corresponding to the inequality 1⊤w≤u, which is called the last row (see
-Figure 6). The number of rows (columns) of basis matrix B, denoted by n′,
-is less than or equal to n+1.
-Cramer’s rule states that ( q∗,w∗⊤,u∗) =|det(B)|(1,w′⊤,u′) is a non-
-negative integer vector. It is easy to see that ( q∗,w∗⊤,u∗) satisfies
-A(W)w∗=|det(B)|A(W)w′≥ |det(B)|1=q∗1,
-A(L)w∗=|det(B)|A(L)w′≤ |det(B)|1=q∗1,and
-1⊤w∗=|det(B)|1⊤w′≤ |det(B)|u′=u∗.
-From the above, ( q∗;w∗⊤) is an integer vector of a rough voting represen-
-tation. Assumption N∈ Wimplies that 1⊤w∗≥q∗=|det(B)| ≥1.
-Letd′
-Bbe a subvector of the right-hand-side vector of an inequalit y sys-
-tem in P4 corresponding to rows of B. Cramer’s rule states that det( B)u′=
-det(Bu),whereBuis obtained from Bbut the column corresponding to a
-basic variable uis replaced by d′
-B(see Figure 6). We multiply rows of Bu
-that correspond to losing coalitions by ( −1) and multiply the last row by
-(−1). The resulting matrix, denoted by B′
-u, is a 0–1 matrix whose last row
-includes exactly one 0-component (indexed by u). Lemma 2.1 (c2) implies
-that|det(B′
-u)| ≤2αn′−1≤2αn. Thus, we obtain that
-1⊤w∗≤u∗≤ |u∗|=|det(B)u′|=|det(Bu)|=|det(B′
-u)| ≤2αn.
-By analogy with the proof of Theorem 3.1, we can prove the desi red inequal-
-ities:q∗=|det(B)| ≤αnandw∗
-i≤αn−1(∀i∈N). QED
-19w1w2w3w4w5u
-1 1 1 0 1 0
-0 1 0 1 1 0
-0−1−1 0 0 0
-−1 0 0 −1−10
-0−1 0 −1 0 0
-−1−1−1−1−11
-Bw1w2w3w4w5u
-1 1 1 0 1 1
-0 1 0 1 1 1
-0−1−1 0 0 −1
-−1 0 0 −1−1−1
-0−1 0 −1 0 −1
-−1−1−1−1−10
-Buw1w2w3w4w5u
-1 1 1 0 1 1
-0 1 0 1 1 1
-0 1 1 0 0 1
-1 0 0 1 1 1
-0 1 0 1 0 1
-1 1 1 1 1 0
-B′
-u
-Figure 6: Examples of elementary matrix operations for P4.
-6 Conclusion
-In this paper, we discussed the smallest value of k∗such that every k∗-trade
-robust simple game would be weighted. We provided a new proof of the
-existence of a trading transform when a given simple game is n on-weighted.
-Our proof yields an improved upper bound on the required leng th of a
-trading transform. We showed that a given simple game Gis weighted if
-and only if Gisαn+1-trade robust, where αn+1denotes the maximal value
-of determinants of ( n+1)×(n+1) 0–1 matrices. Applying the Hadamard’s
-evaluation [Hadamard, 1893] of the determinant, we obtain k∗≤αn+1≤
-(n+2)n+2
-2(1/2)(n+1), which improves the existing bound k∗≤(n+1)nn/2
-obtained by [Gvozdeva and Slinko, 2011].
-Next, we discussed upper bounds for the maximum possible int eger
-weights and the quota needed to represent any weighted simpl e game with n
-players. We show that every weighted simple game (satisfyin g∅ /\e}atio\slash∈ W ∋ N)
-has an integer-weight representation ( q;w⊤)∈Z×ZNsuch that |wi| ≤αn
-(∀i∈N),|q| ≤αn+1, and 1≤/summationtext
-i∈Nwi≤2αn+1−1. We demonstrated the
-tightness of our bound on the quota when n≤5.
-We described a rounding method based on a linear relaxation o f an
-integer programming problem for finding an integer-weight r epresentation.
-We showed that an integer-weight representation is obtaine d by carefully
-rounding a solution of the linear inequality system multipl ied byλ•≤
-(2−√
-2)n+(√
-2−1)<0.5858n+0.4143. Our proof of Theorem 4.1 indicates
-an existence of a randomized rounding algorithm for finding a n appropriate
-valueλ•. However, from theoretical point of view, Theorem 4.1 only s howed
-the existence of a real number λ•. Even if there exists an appropriate “ratio-
-nal” number λ•, we need to determine the size of the rational number (its
-numerator and denominator) to implement a naive randomized rounding
-algorithm. Thus, it remains open whether there exists an effic ient algo-
-20rithm for finding an integer-weight representation satisfy ing the properties
-in Theorem 4.1.
-Lastly, we showed that a roughly weighted simple game (satis fying∅ /\e}atio\slash∈
-W ∋N) has an integer vector ( q;w⊤) of the rough voting representation
-satisfying 0 ≤wi≤αn−1(∀i∈N), 0≤q≤αn, and 1≤/summationtext
-i∈Nwi≤2αn.
-When a given simple game is not roughly weighted, we showed th at (under
-the the monotonicity property (1) and ∅ /\e}atio\slash∈ W ∋ N) there existed a potent
-certificate of non-weightedness whose length is less than or equal to 2 αn+1.
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-23

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@@ -1,1202 +0,0 @@
-JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 1
-Attention-Based Neural Networks for Chroma Intra
-Prediction in Video Coding
-Marc G ´orriz Blanch, Student Member IEEE, Saverio Blasi, Alan F. Smeaton, Fellow IEEE,
-Noel E. O’Connor, Member IEEE, and Marta Mrak, Senior Member IEEE
-Abstract —Neural networks can be successfully used to im-
-prove several modules of advanced video coding schemes. In
-particular, compression of colour components was shown to
-greatly benefit from usage of machine learning models, thanks
-to the design of appropriate attention-based architectures that
-allow the prediction to exploit specific samples in the reference
-region. However, such architectures tend to be complex and
-computationally intense, and may be difficult to deploy in a
-practical video coding pipeline. This work focuses on reducing
-the complexity of such methodologies, to design a set of simpli-
-fied and cost-effective attention-based architectures for chroma
-intra-prediction. A novel size-agnostic multi-model approach is
-proposed to reduce the complexity of the inference process. The
-resulting simplified architecture is still capable of outperforming
-state-of-the-art methods. Moreover, a collection of simplifications
-is presented in this paper, to further reduce the complexity
-overhead of the proposed prediction architecture. Thanks to
-these simplifications, a reduction in the number of parameters
-of around 90% is achieved with respect to the original attention-
-based methodologies. Simplifications include a framework for re-
-ducing the overhead of the convolutional operations, a simplified
-cross-component processing model integrated into the original
-architecture, and a methodology to perform integer-precision
-approximations with the aim to obtain fast and hardware-aware
-implementations. The proposed schemes are integrated into the
-Versatile Video Coding (VVC) prediction pipeline, retaining
-compression efficiency of state-of-the-art chroma intra-prediction
-methods based on neural networks, while offering different
-directions for significantly reducing coding complexity.
-Index Terms —Chroma intra prediction, convolutional neural
-networks, attention algorithms, multi-model architectures, com-
-plexity reduction, video coding standards.
-I. I NTRODUCTION
-EFFICIENT video compression has become an essential
-component of multimedia streaming. The convergence
-of digital entertainment followed by the growth of web ser-
-vices such as video conferencing, cloud gaming and real-time
-high-quality video streaming, prompted the development of
-advanced video coding technologies capable of tackling the
-increasing demand for higher quality video content and its con-
-sumption on multiple devices. New compression techniques
-enable a compact representation of video data by identifying
-Manuscript submitted July 1, 2020. The work described in this paper has
-been conducted within the project JOLT funded by the European Union’s Hori-
-zon 2020 research and innovation programme under the Marie Skłodowska
-Curie grant agreement No 765140.
-M. G ´orriz Blanch, S. Blasi and M. Mrak are with BBC Research &
-Development, The Lighthouse, White City Place, 201 Wood Lane, Lon-
-don, UK (e-mail: [email protected], [email protected],
-[email protected]).
-A. F. Smeaton and N. E. O’Connor are with Dublin City University, Glas-
-nevin, Dublin 9, Ireland (e-mail: [email protected], [email protected]).
-Fig. 1. Visualisation of the attentive prediction process. For each reference
-sample 0-16 the attention module generates its contribution to the prediction
-of individual pixels from a target 44block.
-and removing spatial-temporal and statistical redundancies
-within the signal. This results in smaller bitstreams, enabling
-more efficient storage and transmission as well as distribution
-of content at higher quality, requiring reduced resources.
-Advanced video compression algorithms are often complex
-and computationally intense, significantly increasing the en-
-coding and decoding time. Therefore, despite bringing high
-coding gains, their potential for application in practice is
-limited. Among the current state-of-the-art solutions, the next
-generation Versatile Video Coding standard [1] (referred to as
-VVC in the rest of this paper), targets between 30-50% better
-compression rates for the same perceptual quality, supporting
-resolutions from 4K to 16K as well as 360videos. One
-fundamental component of hybrid video coding schemes, intra
-prediction, exploits spatial redundancies within a frame by
-predicting samples of the current block from already recon-
-structed samples in its close surroundings. VVC allows a
-large number of possible intra prediction modes to be usedarXiv:2102.04993v1  [eess.IV]  9 Feb 2021JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 2
-on the luma component at the cost of a considerable amount
-of signalling data. Conversely, to limit the impact of mode
-signalling, chroma components employ a reduced set of modes
-[1].
-In addition to traditional modes, more recent research intro-
-duced schemes which further exploit cross-component correla-
-tions between the luma and chroma components. Such corre-
-lations motivated the development of the Cross-Component
-Linear Model (CCLM, or simply LM in this paper) intra
-modes. When using CCLM, the chroma components are
-predicted from already reconstructed luma samples using a
-linear model. Nonetheless, the limitation of simple linear
-predictions comes from its high dependency on the selection
-of predefined reference samples. Improved performance can
-be achieved using more sophisticated Machine Learning (ML)
-mechanisms [2], [3], which are able to derive more complex
-representations of the reference data and hence boost the
-prediction capabilities.
-Methods based on Convolutional Neural Networks (CNNs)
-[2], [4] provided significant improvements at the cost of two
-main drawbacks: the associated increase in system complex-
-ity and the tendency to disregard the location of individual
-reference samples. Related works deployed complex neural
-networks (NNs) by means of model-based interpretability
-[5]. For instance, VVC recently adopted simplified NN-based
-methods such as Matrix Intra Prediction (MIP) modes [6]
-and Low-Frequency Non Separable Transform (LFNST) [7].
-For the particular task of block-based intra-prediction, the
-usage of complex NN models can be counterproductive if
-there is no control over the relative position of the reference
-samples. When using fully-connected layers, all input samples
-contribute to all output positions, and after the consecutive
-application of several hidden layers, the location of each
-input sample is lost. This behaviour clearly runs counter
-to the design of traditional approaches, in which predefined
-directional modes carefully specify which boundary locations
-contribute to each prediction position. A novel ML-based
-cross-component intra-prediction method is proposed in [4],
-introducing a new attention module capable of tracking the
-contribution of each neighbouring reference sample when
-computing the prediction of each chroma pixel, as shown in
-Figure 1. As a result, the proposed scheme better captures
-the relationship between the luma and chroma components,
-resulting in more accurate prediction samples. However, such
-NN-based methods significantly increase the codec complex-
-ity, increasing the encoder and decoder times by up to 120%
-and 947%, respectively.
-This paper focuses on complexity reduction in video coding
-with the aim to derive a set of simplified and cost-effective
-attention-based architectures for chroma intra-prediction. Un-
-derstanding and distilling knowledge from the networks en-
-ables the implementation of less complex algorithms which
-achieve similar performance to the original models. Moreover,
-a novel training methodology is proposed in order to design a
-block-independent multi-model which outperforms the state-
-of-the-art attention-based architectures and reduces inference
-complexity. The use of variable block sizes during training
-helps the model to better generalise on content variety whileensuring higher precision on predicting large chroma blocks.
-The main contributions of this work are the following:
-A competitive block-independent attention-based multi-
-model and training methodology;
-A framework for complexity reduction of the convolu-
-tional operations;
-A simplified cross-component processing model using
-sparse auto-encoders;
-A fast and cost-effective attention-based multi-model with
-integer precision approximations.
-This paper is organised as follows: Section II provides a
-brief overview on the related work, Section III introduces
-the attention-based methodology in detail and establishes the
-mathematical notation for the rest of the paper, Section IV
-presents the proposed simplifications and Section V shows
-experimental results, with conclusion drawn in Section VI.
-II. B ACKGROUND
-Colour images are typically represented by three colour
-components (e.g. RGB, YCbCr). The YCbCr colour scheme
-is often adopted by digital image and video coding standards
-(such as JPEG, MPEG-1/2/4 and H.261/3/4) due to its ability
-to compact the signal energy and to reduce the total required
-bandwidth. Moreover, chrominance components are often sub-
-sampled by a factor of two to conform to the YCbCr 4:2:0
-chroma format, in which the luminance signal contains most of
-the spatial information. Nevertheless, cross-component redun-
-dancies can be further exploited by reusing information from
-already coded components to compress another component.
-In the case of YCbCr, the Cross-Component Linear model
-(CCLM) [8] uses a linear model to predict the chroma signal
-from a subsampled version of the already reconstructed luma
-block signal. The model parameters are derived at both the
-encoder and decoder sides without needing explicit signalling
-in the bitstream.
-Another example is the Cross-Component Prediction (CCP)
-[9] which resides at the transform unit (TU) level regardless
-of the input colour space. In case of YCbCr, a subsampled and
-dequantised luma transform block (TB) is used to modify the
-chroma TB at the same spatial location based on a context
-parameter signalled in the bitstream. An extension of this
-concept modifies one chroma component using the residual
-signal of the other one [10]. Such methodologies significantly
-improved the coding efficiency by further exploiting the cross-
-component correlations within the chroma components.
-In parallel, recent success of deep learning application
-in computer vision and image processing influenced design
-of novel video compression algorithms. In particular in the
-context of intra-prediction, a new algorithm [3] was introduced
-based on fully-connected layers and CNNs to map the predic-
-tion of block positions from the already reconstructed neigh-
-bouring samples, achieving BD-rate (Bjontegaard Delta rate)
-[11] savings of up to 3.0% on average over HEVC, for approx.
-200% increase in decoding time. The successful integration
-of CNN-based methods for luma intra-prediction into existing
-codec architectures has motivated research into alternative
-methods for chroma prediction, exploiting cross-componentJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 3
-Fig. 2. Baseline attention-based architecture for chroma intra prediction presented in [4] and described in Section III.
-redundancies similar to the aforementioned LM methods. A
-novel hybrid neural network for chroma intra prediction was
-recently introduced in [2]. A first CNN was designed to
-extract features from reconstructed luma samples. This was
-combined with another fully-connected network used to extract
-cross-component correlations between neighbouring luma and
-chroma samples. The resulting architecture uses complex non-
-linear mapping for end-to-end prediction of chroma channels.
-However, this is achieved at the cost of disregarding the spatial
-location of the boundary reference samples and significant
-increase of the complexity of the prediction process. As shown
-in [4], after a consecutive application of fully-connected layers
-in [2], the location of each input boundary reference sample
-is lost. Therefore, the fully-convolutional architecture in [4]
-better matches the design of the directional VVC modes and
-is able to provide significantly better performance.
-The use of attention models enables effective utilisation
-of the individual spatial location of the reference samples
-[4]. The concept of “attention-based” learning is a well-
-known idea used in deep learning frameworks, to improve the
-performance of trained networks in complex prediction tasks
-[12], [13], [14]. In particular, self-attention is used to assess the
-impact of particular input variables on the outputs, whereby
-the prediction is computed focusing on the most relevant
-elements of the same sequence [15]. The novel attention-
-based architecture introduced in [4] reports average BD-rate
-reductions of -0.22%, -1.84% and -1.78% for the Y , Cb and
-Cr components, respectively, although it significantly impacts
-the encoder and decoder time.
-One common aspect across all related work is that whilst
-the result is an improvement in compression this comes at the
-expense of increased complexity of the encoder and decoder.
-In order to address the complexity challenge, this paper aims
-to design a set of simplified attention-based architectures for
-performing chroma intra-prediction faster and more efficiently.
-Recent works addressed complexity reduction in neural net-
-works using methods such as channel pruning [16], [17],
-[18] and quantisation [19], [20], [21]. In particular for videocompression, many works used integer arithmetic in order
-to efficiently implement trained neural networks on different
-hardware platforms. For example, the work in [22] proposes a
-training methodology to handle low precision multiplications,
-proving that very low precision is sufficient not just for
-running trained networks but also for training them. Similarly,
-the work in [23] considers the problem of using variational
-latent-variable models for data compression and proposes
-integer networks as a universal solution of range coding as
-an entropy coding technique. They demonstrate that such
-models enable reliable cross-platform encoding and decoding
-of images using variational models. Moreover, in order to
-ensure deterministic implementations on hardware platforms,
-they approximate non-linearities using lookup tables. Finally,
-an efficient implementation of matrix-based intra prediction
-is proposed in [24], where a performance analysis evaluates
-the challenges of deploying models with integer arithmetic
-in video coding standards. Inspired by this knowledge, this
-paper develops a fast and cost-effective implementation of the
-proposed attention-based architecture using integer precision
-approximations. As shown Section V-D, while such approxi-
-mations can significantly reduce the complexity, the associated
-drop of performance is still not negligible.
-III. A TTENTION -BASED ARCHITECTURES
-This section describes in detail the attention-based approach
-proposed in [4] (Figure 2), which will be the baseline for the
-presented methodology in this paper. The section also provides
-the mathematical notation used for the rest of this paper.
-Without loss of generality, only square blocks of pixels
-are considered in this work. After intra-prediction and recon-
-struction of a luma block in the video compression chain,
-luma samples can be used for prediction of co-located chroma
-components. In this discussion, the size of a luma block is
-assumed to be (downsampled to) NNsamples, which is
-the size of the co-located chroma block. This may require the
-usage of conventional downsampling operations, such as in
-the case of using chroma sub-sampled picture formats suchJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 4
-Fig. 3. Proposed multi-model attention-based architectures with the integration of the simplifications introduced in this paper. More details about the model’s
-hyperparameters and a description of the referred schemes can be found in Section V.
-as 4:2:0. Note that a video coding standard treats all image
-samples as unsigned integer values within a certain precision
-range based on the internal bit depth. However, in order to
-utilise common deep learning frameworks, all samples are
-converted to floating point and normalised to values within the
-range [0;1]. For the chroma prediction process, the reference
-samples used include the co-located luma block X02I RNN,
-and the array of reference samples Bc2I Rb,b= 4N+1from
-the left and from above the current block (Figure 1), where
-c=Y,CborCrrefers to the three colour components. B
-is constructed from samples on the left boundary (starting
-from the bottom-most sample), then the corner is added,
-and finally the samples on top are added (starting from the
-left-most sample). In case some reference samples are not
-available, these are padded using a predefined value, following
-the standard approach defined in VVC. Finally, S02I R3b
-is the cross-component volume obtained by concatenating
-the three reference arrays BY,BCbandBCr. Similar to
-the model in [2], the attention-based architecture adopts a
-scheme based on three network branches that are combined to
-produce prediction samples, illustrated in Figure 2. The first
-two branches work concurrently to extract features from the
-input reference samples.
-The first branch (referred to as the cross-component bound-
-ary branch) extracts cross component features from S02
-I R3bby applying Iconsecutive Di- dimensional 11
-convolutional layers to obtain the Si2I RDiboutput feature
-maps, where i= 1;2:::I. By applying 11convolutions, the
-boundary input dimensions are preserved, resulting in an Di-
-dimensional vector of cross-component information for each
-boundary location. The resulting volumes are activated using
-a Rectified Linear Unit (ReLU) non-linear function.
-In parallel, the second branch (referred to as the luma
-convolutional branch) extracts spatial patterns over the co-
-located reconstructed luma block X0by applying convolu-
-tional operations. The luma convolutional branch is defined
-byJconsecutive Cj-dimensional 33convolutional layers
-with a stride of 1, to obtainXj2I RCjN2feature maps fromtheN2input samples, where j= 1;2:::J . Similar to the
-cross-component boundary branch, in this branch a bias and
-a ReLU activation are applied within convolutional layer.
-The feature maps ( SIandXJ) from both branches are
-each convolved using a 11kernel, to project them into
-two corresponding reduced feature spaces. Specifically, SI
-is convolved with a filter WF2I RhDto obtain the h-
-dimensional feature matrix F. Similarly,XJis convolved with
-a filterWG2I RhCto obtain the h-dimensional feature
-matrixG. The two matrices are multiplied together to obtain
-the pre-attention map M=GTF. Finally, the attention matrix
-A2I RN2bis obtained applying a softmax operation to each
-element ofM, to generate the probability of each boundary
-location being able to predict a sample location in the block.
-Each valuej;iinAis obtained as:
-j;i=exp (mi;j=T)
-Pb1
-n=0exp (mn;j=T); (1)
-wherej= 0;:::;N21represents the sample location in
-the predicted block, i= 0;:::;b1represents a reference
-sample location, and Tis the softmax temperature parameter
-controlling the smoothness of the generated probabilities, with
-0<T1. Notice that the smaller the value of T, the more
-localised are the obtained attention areas resulting in corre-
-spondingly fewer boundary samples contributing to a given
-prediction location. The weighted sum of the contribution of
-each reference sample in predicting a given sample at a specific
-location is obtained by computing the matrix multiplication
-between the cross-component boundary features SIand the
-attention matrix A, or formally ST
-IA. In order to further refine
-ST
-IA, this weighted sum can be multiplied by the output of
-the luma branch. To do so, the output of the luma branch
-must be transformed to change its dimensions by means of a
-11convolution using a matrix Wx2I RDCto obtain a
-transformed representation X, thenO=X(ST
-IA), where
-is the element-wise product.
-Finally, the output of the attention model is fed into the third
-network branch, to compute the predicted chroma samples. InJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 5
-this branch, a final CNN is used to map the fused features from
-the first two branches as combined by means of the attention
-model into the final chroma prediction. The prediction head
-branch is defined by two convolutional layers, applying E-
-dimensional 33convolutional filters and then 2-dimensional
-11filters for deriving the two chroma components at once.
-IV. M ULTI -MODEL ARCHITECTURES
-This section introduces a new multi-model architecture
-which improves the baseline attention-based approach (Section
-III, [4]). The main improvement comes from its block-size
-agnostic property as the proposed approach only requires one
-model for all block sizes. Furthermore, a range of simpli-
-fications is proposed with the aim to reduce the complex-
-ity of related attention-based architectures while preserving
-prediction performance as much as possible. The proposed
-simplifications include a framework for complexity reduction
-of the convolutional operations, a simplified cross-component
-boundary branch using sparse autoencoders and insights for
-fast and cost-effective implementations with integer precision
-approximations. Figure 3 illustrates the proposed multi-model
-attention-based schemes with the integration of the simplifica-
-tions described in this section.
-A. Multi-model size agnostic architecture
-In order to handle variable block sizes, previous NN-based
-chroma intra-prediction methods employ different architec-
-tures for blocks of different sizes. These architectures differ
-in the dimensionality of the networks, which depend on give
-block size, as a trade-off between model complexity and
-prediction performance [2]. Given a network structure, the
-depth of the convolutional layers is the most predominant
-factor when dealing with variable input sizes. This means that
-increasingly complex architectures are needed for larger block
-sizes, in order to ensure proper generalisation for these blocks
-which have higher content variety. Such a factor significantly
-increases requirements for inference because of the number of
-multiple architectures.
-In order to streamline the inference process, this work
-proposes a novel multi-model architecture that is independent
-of the input block size. Theoretically, a convolutional filter
-can be applied over any input space. Therefore, the fully-
-convolutional nature of the proposed architecture ( 11kernels
-for the cross-component boundary branch and 33kernels
-for the luma convolutional one) allows the design of a size
-agnostic architecture. As shown in Figure 4, the same task
-can be achieved using multiple models with different input
-sizes sharing the weights, such that a unified set of filters can
-be used a posterior, during inference. The given architecture
-must employ a number of parameters that is sufficiently large
-to ensure proper performance for larger blocks, but not too
-large to incur overfitting for smaller blocks.
-Figure 5 describes the algorithmic methodology employed
-to train the multi-model approach. As defined in Section III,
-the co-located luma block X02I RNNand the cross-
-component volume S02I R3bare considered as inputs to
-the chroma prediction network. Furthermore, for training of a
-Fig. 4. Illustration of the proposed multi-model training and inference
-methodologies. Multiple block-dependent models N(W(t))are used during
-training time. A size-agnostic model with a single set of trained weighs W
-is then used during inference.
-Require:fX(N)
-m,S(N)
-m,Z(N)
-mg,m2[0;M),N2f4;8;16g
-Require:N(W(t)):Nmodel with shared weights W(t)
-Require:L(t)
-reg: Objective function at training step t
-1:t 0(initialise timestep)
-2:whiletnot converged do
-3: form2[0;M)do
-4: forN2f4;8;16gdo
-5: t t+ 1
-6:L(t)
-reg MSE (Z(N)
-m;N(X(N)
-m;S(N)
-m;W(t1)))
-7: g(t) r WL(t)
-reg(get gradients at step t)
-8: W(t) optimiser (g(t))
-9: end for
-10: end for
-11:end while
-Fig. 5. Training algorithm for the proposed multi-model architecture.
-multi-model the ground-truth is defined as Z(N)
-m, for a given
-inputfX(N)
-m;S(N)
-mg, and the set of instances from a database
-ofMsamples or batches is defined as fX(N)
-m;S(N)
-m;Z(N)
-mg,
-wherem= 0;1:::M1andN2f4,8,16gis the set
-of supported square block sizes NN(the method can be
-extended to a different set of sizes). As shown in Figure 4,
-multiple block-dependent models N(W)with shared weights
-Ware updated in a concurrent way following the order of
-supported block sizes. At training step t, the individual model
-N(W(t))is updated obtaining a new set of weights W(t+1).
-Finally, a single set of trained weights Wis used during
-inference, obtaining a size-agnostic model (W). Model pa-
-rameters are updated by minimising the Mean Square Error
-(MSE) regression loss Lreg, as in:
-L(t)
-reg=1
-C
N2kZ(N)
-mN(X(N)
-m;S(N)
-m;W(t1)k2
-2;(2)
-whereC= 2 refers to the number of predicted chroma
-components, and N(W(t1))is the block-dependent model
-at training step t1.JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 6
-Fig. 6. Visualisation of the receptive field of a 2-layer convolutional branch
-with 33kernels. Observe that an output pixel in layer 2is computed by
-applying a 33kernel over a field F1of33samples from the first layer’s
-output space. Similarly, each of the F1values are computed by means of
-another 33kernel looking at a field F0of55samples over the input.
-B. Simplified convolutions
-Convolutional layers are responsible for most of the net-
-work’s complexity. For instance, based on the network hyper-
-parameters from experiments in Section V, the luma convo-
-lutional branch and the prediction head branch (with 33
-convolutional kernels) alone contain 46;882 out of 51;714
-parameters, which constitute more than 90% of the parameters
-in the entire model. Therefore, the model complexity can be
-significantly reduced if convolutional layers can be simpli-
-fied. This subsection explains how a new simplified structure
-beneficial for practical implementation can be devised by
-removing activation functions, i.e. by removing non-linearities.
-It is important to stress that such process is devised only for
-application on carefully selected layers, i.e. for branches where
-such simplification does not significantly reduce expected
-performance.
-Consider specific two-layer convolutional branch (e.g. luma
-convolutional branch from Figure 2) formulated as:
-Y=R(W2R(W1X+b1) +b2) (3)
-whereCiare the number of features in layer i,bi2I RCi
-are biases, KiKiare square convolutional kernel sizes,
-W12I RK2
-1C0C1andW22I RK2
-2C1C2are the weights
-and bias of the first ( i= 1) and the second ( i= 2) layers,
-respectively, C0the dimensions of the input feature map,
-Ris a Rectified Linear Unit (ReLU) non-linear activation
-function anddenotes convolution operation. Input to the
-branch isX2I RN2C0and the result is a volume of features
-Y2I RN2C2, which correspond to X0andX2from Figure 2,
-respectively. Removing non-linearities, the given branch can
-be simplified as:
-^Y=W2(W1X+b1) +b2; (4)
-where it can be observed that a new convolution and bias terms
-can be defined using trained parameters from the two initial
-layers, to form a new single layer:
-^Y=WcX+bc; (5)
-Fig. 7. Visualisation of the learnt colour space resulting of encoding input
-YCbCr colours to the 3-dimensional hidden space of the autoencoder.
-whereWc2I R[^K2C0]C2is the function of W1andW2with
-^K=K1+K21, andbcis a constant vector derived from W2,
-b1andb2. Figure 6 (a) illustrates the operations performed in
-Eq. 4 forK1=K2= 3 andC= 1. Analysing the receptive
-field of the whole branch, a pixel within the output volume Y
-is computed by applying a K2K2kernel over a field F1from
-the first layer’s output space. Similarly, each of the F1values
-are computed by means of another K1K1kernel looking
-at a fieldF0. Without the non-linearities, and equivalent of
-this process is simplified, Figure 6 (b) and Eq. 5. Notice that
-^K=K1+K21equals 5in the example in Figure 6. For a
-variety of parameters, including the values of C0,CiandKi
-used in [4] and in this paper, this simplification of concatenated
-convolutional layers allows reduction of model’s parameters at
-inference time, which will be shown in Section V-C.
-Finally, it should be noted that we limit the removal of
-activation functions only to branches which include more than
-one layer, from which at least one layer has Ki>1, and only
-the activation functions between layers in the same branch are
-removed (to be able to merge them as in Equation 5). In such
-branches with at least one Ki>1the number of parameters
-is typically very high, while the removal of non-linearities
-typically does not impact prediction performance. Activation
-functions are not removed from the remaining layers. It should
-be noted that in the attention module and at the intersections
-of various branches the activation functions are critical and
-therefore are left unchanged. Section V-C performs an ablation
-test to evaluate the effect of removing the non-linearities, and
-a test to evaluate how would a convolutional branch directly
-trained with large-support kernels ^Kperform.
-C. Simplified cross-component boundary branch
-In the baseline model, the cross-component boundary
-branch transforms the boundary inputs S2I R3bintoDJ-
-dimensional feature vectors. More specifically, after applying
-J= 2 consecutive 11convolutional layers, the branch
-encodes each boundary colour into a high dimensional feature
-space. It should be noted that a colour is typically represented
-by 3 components, indexed within a system of coordinates
-(referred to as the colour space). As such, a three-dimensional
-feature space can be considered as the space with minimumJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 7
-dimensionality that is still capable of representing colour
-information. Therefore, this work proposes the use of autoen-
-coders (AE) to reduce the complexity of the cross-component
-boundary branch, by compacting the D-dimensional feature
-space into a reduced, 3-dimensional space. An AE tries to learn
-an approximation to the identity function h(x)xsuch that
-the reconstructed output ^xis as close as possible to the input
-x. The hidden layer will have a reduced dimensionality with
-respect to the input, which also means that the transformation
-process may introduce some distortion, i.e. the reconstructed
-output will not be identical to the input.
-An AE consists of two networks, the encoder fwhich maps
-the input to the hidden features, and the decoder gwhich
-reconstructs the input from the hidden features. Applying this
-concept, a compressed representation of the input can be
-obtained by using the encoder part alone, with the goal of
-reducing the dimensionality of the input vectors. The encoder
-network automatically learns how to reduce the dimensions
-of the input vectors, in a similar fashion to what could be
-obtained applying a manual Principal Component Analysis
-(PCA) transformation. The transformation learned by the AE
-can be trained using the same loss function that is used in the
-PCA process [25]. Figure 7 shows the mapping function of
-the resulting colour space when applying the encoder network
-over the YCbCr colour space.
-Overall, the proposed simplified cross-component boundary
-branch consists of two 11convolutional layers using Leaky
-ReLU activation functions with a slope = 0:2. First, aD-
-dimensional layer is applied over the boundary inputs Sto
-obtainS12I RDbfeature maps. Then, S1is fed to the AE’s
-encoder layer fwith output 3dimensions, to obtain the hidden
-feature maps S22I R3b. Finally, a third 11convolutional
-layer (corresponding to the AE decoder layer g) is applied
-to generate the reconstructed maps ~S1withD-dimensions.
-Notice that the decoder layer is only necessary during the
-training stage to obtain the reconstructed inputs necessary to
-derive the values of the loss function. Only the encoder layer
-is needed when using the network, in order to transform the
-input feature vectors into the 3dimensional, reduced vectors.
-Figure 3 illustrates the branch architecture and its integration
-within the simplified multi-model.
-Finally, in order to interpret the behaviour of the branch
-and to identify prediction patterns, a sparsity constraint can
-be imposed on the loss function. Formally, the following can
-be used:
-LAE=r
-D
bkS1~S1k2
-2+s
-3
bkS2k1; (6)
-where the right-most term is used to keep the activation
-functions in the hidden space remain inactive most of the
-time, and only return non-zero values for the most descriptive
-samples. In order to evaluate the effect of the sparsity term,
-Section V-C performs an ablation test that shows its positive
-regularisation properties during training.
-The objective function in Equation 2 can be updated such
-that the global multi-model loss Lconsiders bothLregand
-LAEas:
-L=regLreg+AELAE (7)whereregandAEcontrol the contribution of both losses.
-D. Integer precision approximation
-While the training algorithm results in IEEE-754 64-bit
-floating point weights and prediction buffers, an additional
-simplification is proposed in this paper whereby the network
-weights and prediction buffers are represented using fixed-
-point integer arithmetic. This is beneficial for deployment of
-resulting multi-models in efficient hardware implementations,
-which complex operations such as Leaky ReLU and softmax
-activation functions can become serious bottlenecks. All the
-network weights obtained after the training stage are therefore
-appropriately quantised to fit 32-bit signed integer values. it
-should be noted that integer approximation introduces quanti-
-sation errors, which may have an impact on the performance
-of the overall predictions.
-In order to prevent arithmetic overflows after performing
-multiplications or additions, appropriate scaling factors are
-defined for each layer during each of the network predic-
-tion steps. To further reduce the complexity of the integer
-approximation, the scaling factor Klfor a given layer lis
-obtained as a power of 2, namelyKl= 2Ol, whereOlis the
-respective precision offset. This ensures that multiplications
-can be performed by means of simple binary shifts. Formally,
-the integer weights ~Wland biases ~blfor each layer lin the
-network with weights Wland biasblcan be obtained as:
-~Wl=bWl
2Olc;~bl=bbl
2Olc: (8)
-The offsetOldepends on the offset used on the previous layer
-Ol1, as well as on an internal offset Oxnecessary to preserve
-as much decimal information as possible, compensating for
-the quantisation that occurred in the previous layer, namely
-Ol=OxOl1.
-Furthermore, in this approach the values predicted by the
-network are also integers. In order to avoid defining large
-internal offsets at each layer, namely having large values of
-Ox, an additional stage of compensation is applied to the
-predicted values, to keep their values in the range of 32-bit
-signed integer. For this purpose, another offset Oyis defined,
-computed as Oy=OxOl. The values generated by layer l
-are then computed as:
-Yl= (( ~WT
-lXl+~bl) + (1<<(Oy1)))>>O y; (9)
-where<< and>> represent the left and right binary shifts,
-respectively, and the offset (1<<(Oy1))is considered to
-reduce the rounding error.
-Complex operations requiring floating point divisions need
-to be approximated to integer precision. The Leaky ReLU
-activation functions applied on the cross-component boundary
-branch use a slope = 0:2which multiplies the negative
-values. Such an operation can be simply approximated by
-defining a new activation function ~A(x)for any input xas
-follows:
-~A(x) =0 : x0
-26
x>> 7 :x<0
-(10)JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 8
-Conversely, the softmax operations used in the attention
-module are approximated following a more complex method-
-ology, similar to the one used in [26]. Consider the matrix M
-as defined in Equation 1 and a given row jinM, and a vector
-mjas input to the softmax operation. First, all elements mj
-in a row are subtracted by the maximum element in the row,
-namely:
-^mi;j= (mi;j=Tmax i(mi;j=T)) (11)
-whereTis the temperature of the softmax operation, set to 0:5
-as previously mentioned. The transformed elements ^mi;jrange
-between the minimum signed integer value and zero, because
-the arguments ^mi;jare obtained by subtracting the elements
-inMby the maximum element in each row. To further reduce
-the possibility of overflows, this range is further clipped to a
-minimum negative value, set to pre-determined number Ve, so
-that any ^mi;j<Veis set equal to Ve.
-The elements ^mi;jare negative integer numbers within the
-range [Ve;0], meaning there is a fixed number of Ne=jVej+
-1possible values they can assume. To further simplify the
-process, such an exponential function is replaced by a pre-
-computed look-up table containing Neinteger elements. To
-minimise the approximation error, the exponentials are scaled
-by a given scaling factor before being approximated to the
-nearest integer and stored in the corresponding look-up table
-LUT-EXP . Formally, for a given index k, where 0k
-Ne1, thek-th integer input is obtained as sk=Ve+k. The
-k-th element in the look-up table can then be computed as the
-approximated, scaled exponential value for sk, or:
-LUT-EXP (k) =bKeeskc (12)
-whereKe= 2Oeis the scaling factor, chosen in a way to
-maximise the preservation of the original decimal information.
-When using the look-up table during the prediction process,
-given an element ^mi;jthe corresponding index kcan be
-retrieved as: k=jVe^mi;jj, to produce the numerator
-in the softmax function.
-The integer approximation of the softmax function can then
-be written as:
-^j;i=LUT-EXP (jVe^mi;jj)
-D(j); (13)
-where:
-D(j) =b1X
-n=0LUT-EXP (jVe^mn;jj); (14)
-Equation 13 implies performing an integer division between
-the numerator and denominator. This is not ideal, and integer
-divisions are typically avoided in low complexity encoder
-implementations. A simple solution to remove the integer
-division can be obtained by replacing it with a binary shift.
-However, a different approach is proposed in this paper to
-provide a more robust approximation that introduces smaller
-errors in the division. The denominator D(j)as in Equation 14
-is obtained as the sum of bvalues extracted from LUT-EXP ,
-wherebis the number of reference samples extracted from
-the boundary of the block. As such, the largest blocks under
-consideration ( 1616) will result in the largest possible value
-of reference samples bMAX . This means that the maximumvalue that this denominator can assume is obtained when
-b=bMAX and when all input ^mi;j= 0 (which correspond
-toLUT-EXP (jVej) =Ke), corresponding to Vs=bMAXKe.
-Similarly, the minimum value (obtained when ^mi;j=Ve) is0.
-Correspondingly, D(j), can assume any positive integer value
-in the range [0;Vs].
-Considering a given scaling factor Ks= 2Os, integer divi-
-sion byD(j)can be approximated using a multiplication by
-the factorM(j) =bKs=D(j)c. A given value of M(j)could
-be computed for all Vs+1possible values of D(j). Such values
-can then be stored in another look-up table LUT-SUM . Clearly
-though,Vsis too large which means LUT-SUM would be
-impractical to use due to storage and complexity constraints.
-For that reason, a smaller table is used, obtained by quantising
-the possible values of D(j). A pre-defined step Qis used,
-resulting in Ns= (Vs+ 1)=Qquantised values of D(j). The
-table LUT-SUM of sizeNsis then filled accordingly, where
-each element in the table is obtained as:
-LUT-SUM (l) =bKs=(lQ)c (15)
-Finally, when using the table during the prediction process,
-given an integer sum D(j), the corresponding index lcan
-be retrieved as: l=bD(j)=Qc. Following from these
-simplifications, given an input ^mi;jobtained as in Equation 11,
-the integer sum D(j)obtained from Equation 14, and a
-quantisation step Q, the simplified integer approximation of
-the softmax function can eventually be obtained as:
-~j;i=LUT-EXP (jVe^mi;jj)
LUT-SUM (bD(j)=Qc);(16)
-Notice that ~j;ivalues are finally scaled by Ko=Ke
Ks.
-V. E XPERIMENTS
-A. Training settings
-Training examples were extracted from the DIV2K dataset
-[27], which contains high-definition high-resolution content of
-large diversity. This database contains 800 training samples
-and100 samples for validation, providing 6lower resolution
-versions with downsampling by factors of 2,3and4with
-a bilinear and unknown filters. For each data instance, one
-resolution was randomly selected and then M blocks of each
-NNsizes (N= 4;8;16) were chosen, making balanced sets
-between block sizes and uniformed spatial selections within
-each image. Moreover, 4:2:0 chroma sub-sampling is assumed,
-where the same downsampling filters implemented in VVC are
-used to downsample co-located luma blocks to the size of the
-corresponding chroma block. All the schemes were trained
-from scratch using the Adam optimiser [28] with a learning
-rate of 104.
-B. Integration into VVC
-The methods introduced in the paper where integrated
-within a VVC encoder, using the VVC Test Model (VTM)
-7.0 [29]. The integration of the proposed NN-based cross-
-component prediction into the VVC coding scheme requires
-normative changes not only in the prediction process, but also
-in the way the chroma intra-prediction mode is signalled in
-the bitstream and parsed by the decoder.JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 9
-TABLE I
-NETWORK HYPERPARAMETERS DURING TRAINING
-Branch (Cin;KK;C out) Scheme 1 & 3 Scheme 2
-CC Boundary3;11;32
-32;11;323;11;32
-32;11;3
-Luma Convolutional1;33;64
-64;33;641;33;64
-64;33;64
-Attention Module32;11;16
-64;11;16
-64;11;3232;11;16
-64;11;16
-64;11;3
-Prediction Head32;33;32
-32;11;23;33;3
-3;11;2
-TABLE II
-NETWORK HYPERPARAMETERS DURING INFERENCE
-Branch (Cin;KK;C out) Scheme 1 & 3 Scheme 2
-CC Boundary3;11;32
-32;11;323;11;32
-32;11;3
-Luma Convolutional 1;55;64 1;55;64
-Attention Module32;11;16
-64;11;16
-64;11;3232;11;16
-64;11;16
-64;11;3
-Prediction Head 32;33;2 3;33;2
-A new block-level syntax flag is introduced to indicate
-whether a given block makes use of one of the proposed
-schemes. If the proposed NN-based method is used, a pre-
-diction is computed for the two chroma components. No
-additional information is signalled related to the chroma intra-
-prediction mode for the block. Conversely, if the method
-is not used, the encoder proceeds in signalling the chroma
-intra-prediction mode as in conventional VVC specifications.
-For instance, a subsequent flag is signalled to identify if
-conventional LM modes are used in the current block or not.
-The prediction path also needs to accommodate the new NN-
-based predictions. This largely reuses prediction blocks that
-are needed to perform conventional CCLM modes. In terms
-of mode selection at the encoder side, the new NN-based mode
-is added to the conventional list of modes to be tested in full
-rate-distortion sense.
-C. Architecture configurations
-The proposed multi-model architectures and simplifications
-(Section IV) are implemented in 3 different schemes:
-Scheme 1: Multi-model architecture (Section IV-A) ap-
-plying the methodology in Section IV-B to simplify the
-convolutional layers within the luma convolutional branch
-and the prediction branch, as illustrated in Figure 3.
-Scheme 2: The multi-model architecture in Scheme 1
-applying the methodology in Section IV-C to simplify
-the cross-component boundary branch. As shown in Fig-
-ure 3, the integration of the simplified branch requires
-modification of the initial architecture with changes in
-the attention module and the prediction branch.
-Scheme 3: Architecture in Scheme 1 with the integer
-precision approximations described in Section IV-D.
-In contrast to previous state-of-the-art methods, the pro-
-posed multi-model does not need to adapt its architectureto the input block size. Notice that the fully-convolutional
-architecture introduced in [4] enables this design and is able
-to significantly reduce the complexity of the cross-component
-boundary branch in [2], which uses size-dependent fully-
-connected layers. Table I shows the network hyperparameters
-of the proposed schemes during training, whereas Table II
-shows the resulting hyperparameters for inference after ap-
-plying the proposed simplifications. As shown in Tables III
-and IV, the employed number of parameters in the proposed
-schemes represents the trade-off between complexity and
-prediction performance, within the order of magnitude of
-related attention-based CNNs in [4]. The proposed simplifi-
-cations significantly reduce (around 90%) the original training
-parameters, achieving lighter architectures for inference time.
-Table III show that the inference version of Scheme 2 reduces
-to around 85%, 96% and 99% the complexity of the hybrid
-CNN models in [2] and to around 82%, 96% and 98% the
-complexity of the attention-based models in [4], for 44;88
-and1616input block sizes, respectively. Finally, in order
-to provide more insights about the computational cost and
-compare the proposed schemes with the state-of-the-art meth-
-ods, Table V shows the number of floating point operations
-(FLOPs) for each architecture per block size. The reduction of
-operations (e.g. additions and matrix multiplications) to arrive
-to the predictions is one the predominant factors towards the
-given speedups. Notice the significant reduction of FLOPs for
-the proposed inference models.
-In order to obtain a preliminary evaluation of the proposed
-schemes and to compare their prediction performance with the
-state-of-the-art methods, the trained models were tested on the
-DIV2K validation set (with 100 multi-resolution images) by
-means of averaged PSNR. Test samples were obtained with
-the same methodology as used in Section V-A for generating
-the training dataset. Notice that this test uses the training
-version of the proposed schemes. As shown in Table IV, the
-multi-model approach introduced in Scheme 1 improves the
-attention-based CNNs in [4] for 44and88blocks, while
-only a small performance drop can be observed for 1616
-blocks. However, because of using a fixed architecture for all
-block sizes, the proposed multi-model architecture averages
-the complexity of the individual models in [4] (Table III),
-slightly increasing the complexity of the 44model and
-simplifying the 1616architecture. The complexity reduction
-in the 1616model leads to a small drop in performance. As
-can be observed from Table IV , the generalisation process
-induced by the multi-model methodology ([4] with multi-
-model, compared to [4]) can minimise such drop by distilling
-knowledge from the rest of block sizes, which is especially
-evident for 88blocks where a reduced architecture can
-improve the state-of-the-art performance.
-Finally, the simplifications introduced in Scheme 2 (e.g.
-the architecture changes required to integrate the modified
-cross-component boundary branch within the original model)
-lower the prediction performance of Scheme 1. However,
-the highly simplified architecture is capable of outperforming
-the hybrid CNN models in [2], observing training PSNR
-improvements of an additional 1.30, 2.21 and 2.31 dB for
-44;88and1616input block sizes, respectively. TheJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 10
-TABLE III
-MODEL COMPLEXITY PER BLOCK SIZE
-Model (parameters) 44 88 1616
-Hybrid CNN [2] 24435 96116 369222
-Attention-based CNN [4] 21602 83106 186146
-Scheme 1 & 3 (train/inference) 51714=7074
-Scheme 2 (train/inference) 39371=3710
-TABLE IV
-PREDICTION PERFORMANCE PER BLOCK SIZE
-Model (PSNR) 44 88 1616
-Hybrid CNN [2] 28:61 31:47 33:36
-Attention-based CNN [4] 30:23 33:13 36:13
-[4] with multi-model 30:55 33:21 36:05
-Scheme 1 single layer training 30:36 33:05 35:88
-Scheme 2 without sparsity 29:89 32:66 35:64
-(proposed) Scheme 1 30:54 33:20 35:99
-(proposed) Scheme 2 29:91 32:68 35:67
-combination of attention-based architectures with the proposed
-multi-model methodology (Scheme 1) considerably improves
-the NN-based chroma intra-prediction methods in [2], showing
-training PSNR improvements by additional 1.93, 1.73 and
-2.68 dB for the supported block sizes. In Section V-D it will
-be shown how this relatively small PSNR differences lead to
-significant differences in codec performance.
-Several ablations were performed in order to evaluate the
-effects of the proposed simplifications. First, the effect of
-the multi-model methodology is evaluated by directly con-
-verting the models in [4] to the size-agnostic architecture in
-Scheme 1 but without the simplifications in Section IV-B
-([4] with multi-model). As can be shown in Table IV, such
-methodology improves the 44and88models, with
-special emphasis in the 88case where the number of
-parameters is smaller than in [4]. Moreover, the removal of
-non-linearities towards Scheme 1 does not significantly affect
-the performance, with a negligible PSNR loss of around 0.3
-dB ([4] with multi-model compared with Scheme 1). Secondly,
-in order to evaluate the simplified convolutions methodology
-in Section IV-B, a version of Scheme 1 was trained with
-single-layer convolutional branches with large support kernels
-(e.g. instead of training 2 linear layers with 33kernels and
-then combining them into 55kernels for inference, training
-directly a single-layer branch with 55kernels). Experimental
-results show the positive effects of the proposed methodology,
-observing a significant drop of performance when a single-
-layer trained branch is applied (Scheme 1 with single layer
-training compared with Scheme 1). Finally, the effect of the
-sparse autoencoder of Scheme 2 is evaluated by removing
-the sparsity term in Equation 7. As can be observed, the
-regularisation properties of the sparsity term, i.e. preventing
-large activations, boosts the generalisation capabilities of the
-multi-model and slightly increases the prediction performance
-by around 0.2 dB. (Scheme 2 without sparsity compared with
-Scheme 2).TABLE V
-FLOP S PER BLOCK SIZE
-Model (parameters) 44 88 1616
-Hybrid CNN [2] 51465 187273 711945
-Attention-based CNN [4] 42795 165451 186146
-Scheme 1 & 3 (train/inference) 102859=13770
-Scheme 2 (train/inference) 79103=7225
-D. Simulation Results
-The VVC reference software VTM-7.0 is used as our
-benchmark and our proposed methodology is tested under the
-Common Test Conditions (CTC) [30], using the suggested all-
-intra configuration for VVC with a QP of 22, 27, 32 and 37. In
-order to fully evaluate the performance of the proposed multi-
-models, the encoder configuration is constrained to support
-only square blocks of 44;88and1616pixels.
-A corresponding VVC anchor was generated under these
-conditions. BD-rate is adopted to evaluate the relative com-
-pression efficiency with respect to the latest VVC anchor. Test
-sequences include 26 video sequences of different resolutions:
-38402160 (Class A1 and A2), 19201080 (Class B),
-832480(Class C), 416240(Class D), 1280720(Class
-E) and screen content (Class F). The “EncT” and “DecT” are
-“Encoding Time” and “Decoding Time”, respectively.
-A colour analysis is performed in order to evaluate the
-impact of the chroma channels on the final prediction per-
-formance. As suggested in previous colour prediction works
-[31], standard regression methods for chroma prediction may
-not be effective for content with wide distributions of colours.
-A parametric model which is trained to minimise the Euclidean
-distance between the estimations and the ground truth com-
-monly tends to average the colours of the training examples
-and hence produce desaturated results. As shown in Figure 8,
-several CTC sequences are analysed by computing the loga-
-rithmic histogram of both chroma components. The width of
-the logarithmic histograms is compared to the compression
-performance in Table VI. Gini index [32] is used to quantify
-the width of the histograms, obtained as
-Gini(H) = 1B1X
-b=0 
-H(b)PB1
-k=0H(k)!2
-(17)
-beingHa histogram of Bbins for a given chroma component.
-Notice that the average value between both chroma compo-
-nents is used in Table VI. A direct correlation between Gini
-index and coding performance can be observed in Table VI,
-suggesting that Scheme 1 performs better for narrower colour
-distributions. For instance, the Tango 2 sequence with a Gini
-index of 0.63 achieves an average Y/Cb/Cr BD-rates of -
-0.46%/-8.13%/-3.13%, whereas Campfire with wide colour
-histograms (Gini index of 0.98), obtains average Y/Cb/Cr
-BD-rates of -0.21%/0.14%/-0.88%. Although the distributions
-of chroma channels can be a reliable indicator of prediction
-performance, wide colour distributions may not be the only
-factor in restricting chroma prediction capabilities of proposed
-methods, which can be investigated in future work.
-A summary of the component-wise BD-rate results for
-all the proposed schemes and the related attention-basedJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 11
-Fig. 8. Comparison of logarithmic colour histograms for different sequences.
-TABLE VI
-BD-R ATES (%) SORTED BY GINI INDEX
-SequenceScheme 1GiniY Cb Cr
-Tango2 -0.46 -8.13 -3.13 0.63
-MarketPlace -0.59 -2.46 -3.06 0.77
-FoodMarket4 -0.16 -1.60 -1.55 0.85
-DaylightRoad2 -0.09 -5.74 -1.85 0.89
-Campfire -0.21 0.14 -0.88 0.98
-ParkRunning3 -0.31 -0.73 -0.77 0.99
-approach in [4] is shown in Table VII for all-intra conditions.
-Scheme 1 achieves an average Y/Cb/Cr BD-rates of -0.25%/-
-2.38%/-1.80% compared with the anchor, suggesting that the
-proposed multi-model size agnostic methodology can improve
-the coding performance of the related attention-based block-
-dependent models. Besides improving the coding performance,
-Scheme 1 significantly reduces the encoding (from 212% to
-164%) and decoding (from 2163% to 1302%) times demon-
-strating the positive effect of the inference simplification.
-Finally, the proposed simplifications introduced in Scheme
-2 and Scheme 3 further reduce the encoding and decoding time
-at the cost of a drop in the coding performance. In particular,
-the simplified cross-component boundary branch introduced
-in Scheme 2, achieves an average Y/Cb/Cr BD-rates of -
-0.13%/-1.56%/-1.63% and, compared to Scheme 1, reduces the
-encoding (from 164% to 146%) and decoding (from 1302%
-to 665%) times. Scheme 3 has lower reduction of encoding
-time (154%) than Scheme 2, but it achieves higher reduction
-in decoding time (665%), although the integer approximations
-lowers the performance achieving average Y/Cb/Cr BD-rates
-of -0.16%/-1.72%/-1.38%.
-As described in Section IV, the simplified schemes in-troduced here tackle the complexity reduction of Scheme 1
-with two different methodologies. Scheme 2 proposes direct
-modifications on the original architecture which need to be
-retrained before being integrated in the prediction pipeline.
-Conversely, Scheme 3 directly simplifies the final prediction
-process by approximating the already trained weights from
-Scheme 1 with integer-precision arithmetic. Therefore, the
-simulation results suggest that the methodology in Scheme
-3 is better at retaining the original performance since a
-retraining process is not required. However, the highly reduced
-architecture in Scheme 2 is capable of approximating the
-performance of Scheme 3 and further reduce the decoder time.
-Overall, the comparison results in Table VII demonstrate
-that proposed models offer various trade-offs between com-
-pression performance and complexity. While it has been shown
-that the complexity can be significantly reduced, it is still not
-negligible. Challenges for future work include integerisation
-of the simplified scheme (Scheme 2) while preventing the
-compression drop observed for Scheme 3. Recent approaches,
-including a published one which focuses on intra prediction
-[24], demonstrate that sophisticated integerisation approaches
-can help retain compression performance of originally trained
-models while enabling them to become significantly less com-
-plex and thus be integrated into future video coding standards.
-VI. C ONCLUSION
-This paper showcased the effectiveness of attention-based
-architectures in performing chroma intra-prediction for video
-coding. A novel size-agnostic multi-model and its corre-
-sponding training methodology were proposed to reduce the
-inference complexity of previous attention-based approaches.
-Moreover, the proposed multi-model was proven to better
-generalise to variable input sizes, outperforming state-of-the-
-art attention-based models with a fixed and much simpler
-architecture. Several simplifications were proposed to further
-reduce the complexity of the original multi-model. First,
-a framework for reducing the complexity of convolutional
-operations was introduced and was able to derive an infer-
-ence model with around 90% fewer parameters than its rela-
-tive training version. Furthermore, sparse autoencoders were
-applied to design a simplified cross-component processing
-model capable of further reducing the coding complexity
-of its preceding schemes. Finally, algorithmic insights were
-proposed to approximate the multi-model schemes in integer-
-precision arithmetic, which could lead to fast and hardware-
-aware implementations of complex operations such as softmax
-and Leaky ReLU activations.
-The proposed schemes were integrated into the VVC an-
-chor VTM-7.0, signalling the prediction methodology as a
-new chroma intra-prediction mode working in parallel with
-traditional modes towards predicting the chroma component
-samples. Experimental results show the effectiveness of the
-proposed methods, retaining compression efficiency of pre-
-viously introduced neural network models, while offering 2
-different directions for significantly reducing coding complex-
-ity, translated to reduced encoding and decoding times. As
-future work, we aim to implement a complete multi-modelJOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, OCTOBER 2020 12
-TABLE VII
-BD-R ATE(%) OFY,Cb ANDCr FOR ALL PROPOSED SCHEMES AND [4] UNDER ALL -INTRA COMMON TESTCONDITIONS
-Class A1 Class A2 Class B Class C
-Y Cb Cr Y Cb Cr Y Cb Cr Y Cb Cr
-Scheme 1 -0.28 -3.20 -1.85 -0.25 -3.11 -1.54 -0.26 -2.28 -2.33 -0.30 -1.92 -1.57
-Scheme 2 -0.08 -1.24 -1.26 -0.12 -1.59 -1.31 -0.15 -1.80 -2.21 -0.20 -1.41 -1.62
-Scheme 3 -0.19 -2.25 -1.56 -0.13 -2.44 -1.12 -0.16 -1.78 -2.05 -0.20 -1.44 -1.29
-Anchor + [4] -0.26 -2.17 -1.96 -0.22 -2.37 -1.64 -0.23 -2.00 -2.17 -0.26 -1.64 -1.41
-Class D Class E Class F OverallEncT[%] DecT[%]Y Cb Cr Y Cb Cr Y Cb Cr Y Cb Cr
-Scheme 1 -0.29 -1.70 -1.77 -0.13 -1.59 -1.45 -0.50 -1.58 -1.99 -0.25 -2.38 -1.80 164% 1302%
-Scheme 2 -0.18 -1.42 -1.73 -0.08 -1.67 -1.40 -0.34 -1.50 -1.90 -0.13 -1.56 -1.63 146% 665%
-Scheme 3 -0.20 -1.64 -1.41 -0.07 -0.75 -0.46 -0.37 -1.24 -1.43 -0.16 -1.72 -1.38 154% 512%
-Anchor + [4] -0.25 -1.55 -1.67 -0.03 -1.35 -1.77 -0.44 -1.30 -1.55 -0.21 -1.90 -1.81 212% 2163%
-for all VVC block sizes in order to ensure a full usage
-of the proposed approach building on the promising results
-shown in the constrained test conditions. Finally, an improved
-approach for integer approximations may enable the fusion of
-all proposed simplifications, leading to a fast and powerful
-multi-model.
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txt/2103.02372.txt

@@ -1,655 +0,0 @@
-Root cause prediction based on bug reports
-Thomas Hirsch
-Institute of Software Technology
-Graz University of Technology
-Graz, Austria
-[email protected] Hofer
-Institute of Software Technology
-Graz University of Technology
-Graz, Austria
-[email protected]
-Abstract —This paper proposes a supervised machine learning
-approach for predicting the root cause of a given bug report.
-Knowing the root cause of a bug can help developers in the de-
-bugging process—either directly or indirectly by choosing proper
-tool support for the debugging task. We mined 54 755 closed
-bug reports from the issue trackers of 103 GitHub projects and
-applied a set of heuristics to create a benchmark consisting of
-10 459 reports. A subset was manually classified into three groups
-(semantic, memory, and concurrency) based on the bugs’ root
-causes. Since the types of root cause are not equally distributed, a
-combination of keyword search and random selection was applied.
-Our data set for the machine learning approach consists of 369 bug
-reports (122 concurrency, 121 memory, and 126 semantic bugs).
-The bug reports are used as input to a natural language processing
-algorithm. We evaluated the performance of several classifiers
-for predicting the root causes for the given bug reports. Linear
-Support Vector machines achieved the highest mean precision
-(0.74) and recall (0.72) scores. The created bug data set and
-classification are publicly available.
-Index Terms —bug report, bug benchmark, root cause prediction
-I. I NTRODUCTION
-Debugging is one of the most time-consuming parts in the
-software development process. While there exist numerous
-fault localization [1] and repair [2] techniques to support
-programmers in the debugging process, it is often unclear which
-techniques work best for a given bug. For this reason, Sobreira et
-al.[3] investigated the structure of Defects4J [4] bugs. For each
-bug, they determined the size of the patch, the repair action,
-and the change pattern. They have invited other researchers to
-investigate which types of bugs1can be handled by their repair
-tools.
-In this paper, we change the perspective of this research topic:
-instead of only providing root cause information for a bench-
-mark to help researchers in evaluating their tools, we predict
-the root cause for a given bug description so that programmers
-can choose a proper tool for their debugging problem. There
-are tools that focus on concurrency (e.g. ConcBugAssist [5])
-or memory (e.g. Valgrind) bugs, while others are better suited
-for semantic bugs (e.g. Jaguar [6]). While some root causes
-can easily be determined when reading a bug report, other
-0©2020 IEEE. Personal use of this material is permitted. Permission from
-IEEE must be obtained for all other uses, in any current or future media,
-including reprinting/republishing this material for advertising or promotional
-purposes, creating new collective works, for resale or redistribution to servers
-or lists, or reuse of any copyrighted component of this work in other works.
-1http://program-repair.org/defects4j-dissection/root causes are not that obvious. Consider for example issue
-ticket #514 from TwelveMonkeys project2:
-TIFF: Invalid StripByteCounts when writing large resolution
-files (9800x8000)
-Hello, when writing a high resolution tiff file the stripByteCounts
-appears to be corrupt. An approx 300 mb output file has a single
-image strip with the byte count of: 4071696385 which is larger than
-the file itself. However when working with lower (more common)
-resolutions the meta for the image strips is created properly. [. . . ]
-This code creates the file with the incorrect meta data:
-// Input high resolution 48 bit depth final
-InputStream inStream = [. . . ]
-Attaching zipped image: 9800x8000 resolution 48bit depth.zip
-I’ve tested and reproduced the issue with the following versions:
-3.4.1, 3.4.2, 3.4.3
-Thanks in advance,
--Jesse
-Our goal is to provide information to the programmer about
-the root cause of this bug. For instance, the incorrect byte
-count mentioned in this bug report together with the information
-about high resolution can raise suspicion of an integer overflow
-occurring.
-We propose a supervised machine learning (ML) approach
-that uses the bug description from issue tickets to predict the
-root cause of the bug. For processing the text from the issue
-tickets, we make use of natural language processing (NLP). For
-creating the training set, we have mined bug reports from 103
-GitHub projects and manually examined a subset, classifying
-them as memory, concurrency or semantic bugs based on the
-actual fix. Since the number of concurrency and memory bugs
-is usually very low [7], we have performed a keyword search in
-the commit messages of fixes to find more instances with these
-root causes.
-While the primary goal of this paper is the root cause
-prediction approach, the generated training data can be used
-as a benchmark for specific types of faults. Often, researchers
-focus on certain bug types when developing a fault localization
-or repair method. While these approaches have a high potential,
-their evaluation is often limited to a few real-world bugs or
-artificially seeded bugs, as mentioned in [8]. The training data
-set created in this paper can be used as a bug benchmark by
-researchers who are interested in certain types of bugs. It can
-be seen as a Java pendant to the C/C++ benchmark BugBench
-that also distinguishes memory, concurrency, and semantic bugs.
-Furthermore, it can be used to evaluate information retrieval
-based bug localization approaches [9].
-2https://github.com/haraldk/TwelveMonkeys/issues/514
-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000671arXiv:2103.02372v1  [cs.SE]  3 Mar 2021The contributions of this work can be summarized as:
-a machine learning approach for predicting the root cause
-for a given bug report with a mean precision of 0.74 and
-a mean recall of 0.72,
-a data set consisting of 10 459 bug reports and fixes from
-103 GitHub repositories,
-a data set of 122 concurrency, 121 memory, and 269
-semantic bugs with detailed sub-categories, and
-a framework for building such data sets.
-The created data sets, all scripts, and the categorization are
-publicly available.3The structure of this paper is as follows:
-Section II introduces the main root cause categories and their
-sub-categories. Section III explains how we have collected
-closed bug reports and their corresponding fixes. Section IV
-presents the machine learning approach. We discuss the results
-and threats to validity in Section V. Section VI discusses the
-related work and Section VII concludes the paper.
-II. C LASSIFICATION SCHEMA
-We use three main categories and 18 detailed root causes as
-described in Table I. The semantic and memory sub-categories
-are based on Tan et al. [7]; the concurrency sub-categories are
-based on Zhou et al. [10].
-A problem with post mortem bug classification arises through
-often unclear separation of the actual fix from other code
-changes, e.g., commits that include more than one bug fix,
-commits that include the bug fix aside of some refactoring
-or new extension, or bug fixes that are scattered over multiple
-commits. Additionally, it is difficult to distinguish a workaround
-from a fix [11]. All of the above make it hard to correctly
-identify the fix and to properly categorize the root cause. To deal
-with these issues, we have added a confidence value ranging
-from 1-10 that reflects our confidence on the correctness of
-our classification: A confidence level of 10 indicates showcase
-quality; 9 indicates that we are very confident about the main
-category and the subcategory; 8 indicates that we are very
-confident about main category and subcategory assigned, but a
-different subcategory cannot be ruled out with 100 % certainty.
-For example, differentiating “processing” and “missing case”
-is often not possible without having the knowledge of the
-programmer who wrote the code. A confidence level of 7 or be-
-low indicates doubts about the chosen subcategory. Confidence
-levels between 3 and 5 indicate a strong confidence about the
-main category, but the subcategories were not identifiable. A
-confidence level of 2 indicates doubts about the main category
-while a level of 1 indicates that it was not possible to determine
-the main root cause category for the bug.
-III. D ATA ACQUISITION
-In this section, we provide details on the collection of the
-bug data set that builds the basis for creating the training set
-for the machine learning approach.
-Purpose of the data set. The data set should provide a realistic
-distribution of different bug types, and should serve as basis for
-3https://doi.org/10.5281/zenodo.3973048TABLE I
-ROOT CAUSE CATEGORIES AND SUB -CATEGORIES
-Semantic Description
-Exception handl. Missing or improper exception handling.
-Missing case Faults due to unawareness of a certain case or simply
-a forgotten implementation.
-Processing Incorrect implementation (e.g. miscalculations, in-
-correct method output, wrong method/library usage).
-Typo Ambiguous naming, typos in SQL calls/URLs/paths.
-Dependency The code can be built but behaves unexpected be-
-cause of changes in a foreign system (e.g. update of
-utilized library or underlying OS).
-Other All other semantic faults.
-Memory
-Buffer overflow Buffer overflows, not overflowing numeric types.
-Null pointer deref. All null pointer dereferences.
-Uninit. mem. read All uninitialized memory reads except null pointer
-dereference.
-Memory leak Memory leak.
-Dangling pointer Dangling pointer.
-Double free Double free.
-Other All other memory bugs.
-Concurrency
-Order violation Missing or incorrect synchronization, e.g. object is
-dereferenced by thread B before it is initialized by
-thread A.
-Race condition Two or more threads access the same resource with
-at least one being a write access and the access is
-not ordered properly.
-Atomic violation Constraints on the interleaving of operations are
-missing. This happens when atomicity of a certain
-code region was assumed but failed to guarantee
-atomicity in the implementation.
-Deadlock Two or more threads wait for the other one to release
-a resource.
-Other All other concurrency bugs.
-experiments with various fault localization and ML experiments.
-The bugs should be real world Java bugs.
-Project selection. We chose 103 GitHub Java projects to
-source our data set. Primary selection criteria were a well known
-organization driving the project, or the project having a high
-star rating on GitHub. However, the list also contains lesser
-known projects that were already used in other research [4],
-[12], [13]. The selection process was performed manually. All of
-the projects utilize GitHub’s built-in issue tracker together with
-its labeling system, and have at least 100 closed issues identified
-as bugs. The project sizes range from 13k Java LOC (Lines Of
-Code) for HikariCP4to 1.7M Java LOC for Elasticsearch5. The
-full list of mined projects can be found in the online appendix3.
-Bug ticket identification. We identified bugs via the labels
-used in the issue tickets and we only considered closed issue
-tickets. In order to omit feature requests, maintenance tickets
-and other non-bug issues, we only considered issues whose
-labels contain “bug”, “defect”, or “regression”.
-Filtering criteria. GitHub automatically links commits to
-issue tickets based on issue ids and provides this data together
-with issue tickets. We only consider issues for which at least
-4https://github.com/brettwooldridge/HikariCP
-5https://github.com/elastic/elasticsearch
-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000672one commit is linked to the issue, and all linked commits are
-still available in the Git repository. If any of the commits are
-linked to multiple issues, or the commit message suggests that
-the fix is done aside of other changes, the issue is discarded. As
-of writing this, we omit issues whose commits do not contain
-changes in production Java code. We plan to lift this limitation
-to incorporate other root causes, e.g. in the documentation or
-build system in the future.
-We use Gumtree Spoon AST Diff6[14] to create Java
-aware diffs. To manage overwhelming runtime and memory
-requirements arising from the size of the data set, we limit the
-size and number of the commits per issue. We only consider
-issues where the number of commits linked to the issue is
-smaller than 10, the number of files changed per commit is
-smaller than 20, and the number of lines changed per commit
-is smaller than 250. Our analysis shows that these limitations
-only remove less than 3 % of the issues.
-The data set. In total, 54 755 issues have been mined from
-GitHub. Following the filtering criteria described above leaves
-us with 10 459 issues that form the basis for our further
-investigations. This bug data set consists of:
-textual bug report including metadata in form of time-
-stamps and user names,
-all commits associated to each bug report including meta-
-data as commit message and git stats, and
-Java aware diff statistics and location of the changes in
-terms of file, class, and method.
-IV. M ACHINE LEARNING APPROACH
-We employ an NLP approach, vectorizing the textual bug
-reports into unigrams and bigrams, to train a model for auto-
-mated classification along our fault classification schema. This
-approach calculates a frequency vector from words and word
-pairs occurring in the input text that is used as feature vector
-for the classifier.
-Input preprocessing. To increase performance of the classifier,
-we applied the following preprocessing steps:
-Stop word removal (i.e. removing common words that are
-not adding any value)
-Case folding (i.e. converting all characters to lower case)
-Stemming (i.e. reducing each word to its word stem)
-The bug reports often include stack traces, exceptions, and log
-outputs. Currently, we process them in the same way as the
-rest of the input text. In future work, we will investigate the
-usefulness of domain specific preprocessing of these artifacts.
-Training set creation. Figure 1 provides an overview of the
-training set creation. We manually classified 160 randomly
-selected issues and identified 119 semantic, 2 memory, and 4
-concurrency bugs. 35 issues were not classified because the bug
-reports were non-English, feature requests, deemed not a bug, or
-issues for which we were not confident about the sub-category
-(confidence level <8).
-Concurrency and memory bugs are usually rare, accounting
-for 2 % respectively 6 % of all bugs [7], [15], [16], which poses
-6https://github.com/SpoonLabs/gumtree-spoon-ast-diff
-Random sample of 471 issuesFilter criteria:
-•Commits linked
-•Commits only address 
-one issue
-•Change of production 
-Java code
-•Reasonable size for fix
-11954 755 issues labeled as bug
-from 103 GitHub projects
-10 459 complete issues
-Random 
-sample
-of 160 
-issues756 potential memory or
-concurrency bugsRandom 
-selectionKeyword
-search
-150 semantic 
-bugs 
-keyword 
-biased121
-memory 
-bugs122 
-concurrency
-bugs118
-2 4150 119
-Filter criteria:
-•Classification 
-confidence > 7
-•English issue text
-•Bug, not hidden 
-feature request
-119
-semantic 
-Bugs
-369 issues for machine learning approachAll All All5 %
-(7)Random 
-selection
-119Fig. 1. Training set creation
-a challenge for the creation of reasonably sized training sets.
-For this reason, we have performed a keyword search on the
-commit messages linked to the issues to identify candidates of
-memory and concurrency bugs analog to Ray et al. ’s approach
-[15], resulting in a total of 756 issues. As of writing this, 471
-randomly selected issues from this set have been examined and
-classified. 150 semantic, 119 memory, and 118 concurrency
-bugs have been identified in this sample. 84 issues could not be
-classified due to the reasons mentioned above.
-Training set composition. To avoid a bias towards the se-
-mantic bugs that were “accidentally found” during the manual
-classification of the keyword search results and to have approx-
-imately equally large training sets, we reduced their volume
-to 5 % of all semantic bugs. This is a rather high estimate
-given the fact, that only 7.2 % of all bugs have been reported
-in the keyword search and only one third of these bugs are
-actually semantic bugs. Further, using separate data bases for
-the keyword search (commit messages) and training set for our
-ML classifier (bug reports) makes us confident that the bias
-introduced by the keywords is limited. As of writing this, our
-training set consists of 122 concurrency bugs, 121 memory
-bugs, and 126 semantic bugs. The complete training set consists
-of 369 textual bug reports.
-Classifiers. We applied various supervised ML classifier
-algorithms on our data set, namely Multinomial Naive Bayes
-(MNB), Linear Support Vector (LSVC), Linear Support Vector
-with Stochastic Gradient Descent learning (SGDC), Random
-Forrest (RFC), and Logistic Regression (LRC). The selection
-of classifiers is based on their suitability for multi-class clas-
-sification problems based textual inputs, and their application
-in similar research. Support vector machines have been used in
-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000673comparable endeavors [7], [15]–[18]; the same applies to naive
-Bayes [7], [16], [17], [19]–[21], logistic regression [20], [21],
-and decision tree based algorithms [7], [20], [21].
-Experiment. The 369 bug reports were split into a training
-set (80 %) and a test set (20 %). We performed 5-fold cross
-validation on the training set for each classifier, using grid
-search for hyperparameter tuning. Mean accuracy was used as
-scoring metric in the grid search. The highest scoring model for
-each classifier was then evaluated on the test set yielding the
-final score for this classifier. This experiment was performed
-10 times, followed by manual examination of its results and
-corresponding hyperparameters to reduce the grid search space.
-Finally, to enable comparison of the classifiers given the small
-input data set, the above described experiment with the reduced
-hyperparameter set was performed 100 times with randomized
-test and training splits. The employed set of hyperparameters,
-and grid search results for each experiment, can be found in the
-online appendix.
-V. R ESULTS
-A. Classification results
-We graphically compare the classifiers’ performance by
-means of the weighted averages of F1, precision, and recall
-in Figure 2 and we report mean, median, standard deviation,
-min, and max of each classifier in Table II based on the scores
-of 100 runs. Please note that the F1 scores are computed for
-the individual test runs and then the mean, median, standard
-deviation, min, and max values of these F1 scores are computed.
-Thus, they cannot be computed from the precision and recall
-given in the table.
-We observed a tight clustering of classifiers, which is also
-evident in individual runs, although individual runs exhibit
-varying performances. We attribute this behavior to the small
-data set size and high variance in data quality. The best overall
-performance was achieved with LSVC, with mean F1 (0.72),
-precision (0.74), and recall (0.72). LSVC also produced the
-highest observed scores in an individual run, yielding F1 (0.85),
-precision (0.88), and recall (0.85).
-B. Discussion
-The biggest challenge lies in the creation of a reasonably
-sized data set. Further, varying data quality constitutes a sig-
-nificant problem. The textual bug reports in our data set range
-from only 5 words to 60kB of text per report. However, our
-examination of those bug tickets shows that the length is not
-necessarily correlating with the quality in terms of usefulness
-for the developer. Issue #4960 from Elasticsearch7is an example
-of a bug report that requires context and knowledge about the
-project for understanding:
-Filtered query parses name incorrectly
-There are bug reports that merely describe the impact, e.g.
-issue #338 from the Redisson project8:
-7https://github.com/elastic/elasticsearch/issues/4960
-8https://github.com/redisson/redisson/issues/338
-Fig. 2. Mean weighted average precision, recall and F1 score
-TABLE II
-WEIGHTED AVERAGE PRECISION ,RECALL ,AND F1SCORES (100 RUNS )
-Classifier Precision Recall F1
-MeanLRC 0.73 0.71 0.71
-RFC 0.72 0.62 0.62
-SGDC 0.74 0.71 0.71
-LSVC 0.74 0.72 0.72
-MNB 0.72 0.70 0.70
-MedianLRC 0.73 0.70 0.70
-RFC 0.72 0.62 0.62
-SGDC 0.74 0.70 0.71
-LSVC 0.74 0.72 0.72
-MNB 0.73 0.70 0.70
-Std. dev.LRC 0.046 0.048 0.048
-RFC 0.053 0.076 0.082
-SGDC 0.045 0.049 0.049
-LSVC 0.046 0.046 0.046
-MNB 0.045 0.049 0.049
-MinLRC 0.63 0.62 0.62
-RFC 0.53 0.39 0.38
-SGDC 0.64 0.62 0.63
-LSVC 0.65 0.64 0.63
-MNB 0.60 0.57 0.57
-MaxLRC 0.84 0.82 0.82
-RFC 0.85 0.77 0.77
-SGDC 0.86 0.84 0.84
-LSVC 0.88 0.85 0.85
-MNB 0.83 0.82 0.82
-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000674New version 2.1.4 or greater performance is low.
-When I use redisson 2.1.3 the ubuntu’s load average is 1.8 2.3; but
-I use 2.1.4 or greater, the load average is often greater than 3.00,
-my java application often overload.
-In some cases, the bug reports point right away at the fault,
-e.g. Netty issue #18789:
-WebSocket08FrameDecoder leaks ByteBuf when payload is
-masked
-Further research is required to determine metrics to mea-
-sure bug report quality for our purpose. On the other end of
-the spectrum, for very long bug reports, additional text pre-
-processing is required. Heuristics for reduction or removal of
-artifacts have to be implemented. Such artifacts are stack traces,
-code snippets, log outputs, or similar text portions, whose size
-is disproportionate to the added information.
-C. Threats to validity
-The selection of bugs from the issue tickets by searching for
-certain labels is a threat to the internal validity. While we have
-considered a wide range of bug labels, we cannot rule out to
-miss bugs with special labels or wrongly labeled bugs. A study
-on 7000 issue reports from five open-source projects showed
-that up to 40 % of the issues were wrongly labeled [22].
-Manually categorizing the root cause might be error-prone
-and the true root cause of the bug can only be determined
-by the original programmer. For this reason, we indicated the
-confidence level for each bug we categorized and excluded bugs
-with a low confidence level. Furthermore, the fix might be a
-workaround instead of a fix of the true fault.
-The keyword search might only reveal certain types of
-memory and concurrency bugs. We have tried to avoid a bias in
-the classification towards the words used in the keyword search
-by performing the keyword search on the commit messages and
-NLP for classification on the bug description.
-The small sample size is the biggest threat to external validity.
-In future work, we will therefore enlarge the training set. The
-performance of this approach may vary based on the software
-domain of the examined project. We tried to counteract this
-by including software projects to source our data set. However,
-data mining was exclusively performed on open source projects.
-Further, most of the examined projects are libraries. In contrast
-to end-user software, bug reports for libraries are almost ex-
-clusively written by other developers. Such bug reports often
-already contain insights into the underlying problem. Further,
-our approach may not work as good for bug descriptions of
-software written in a different programming language.
-VI. R ELATED WORK
-Ray et al. [15] analyzed more 560 000 bug fixes from
-729 GitHub projects written in 17 languages. They classified
-the root causes and impacts for 10 % of the bugs by searching
-for keywords in the commit messages and trained a supervised
-ML approach to classify the remaining 90 % of the bugs. They
-9https://github.com/netty/netty/issues/1878validated their approach by manual classifying 180 bug fixes
-(83.7 % precision, 84.3 % recall). While we also rely on a
-keyword search, we did not perform the keyword search on
-the same text that was used in NLP to avoid biasing.
-Li and colleagues [16] classified the root cause, impact,
-and software component of nearly 30 000 Bugzilla entries
-using NLP with SVM, Winnow, Perceptron and Naive Bayes
-as classifiers. Their training set consists of 709 bugs (51 %
-randomly sampled, 36 % security-related, and 13 % concurrency
-bugs).
-Tan et al. [7] manually classified 339 bugs from randomly
-selected fixed issues of three open-source projects into the
-dimensions root cause, impact, and component. Because of
-the low number of concurrency bugs in the sample, they
-performed a keyword search to identify additional concurrency
-bugs. Semantic bugs are the dominant root cause with 70-
-87 %. The Linux kernel has nearly 13.6 % concurrency bugs;
-the other projects (Mozilla and Apache) have a lower number
-of concurrency bugs with 1.2 % and 5.2 %. Furthermore, the
-authors automatically classified more than 100 000 bugs using
-a supervised ML (precision: 67 % for memory and 93 % for
-semantic bugs, recall: 57 % resp. 95 %).
-Ortu et al. [17] investigated whether there are differences
-in the characteristics of high and low priority defects in more
-than 1200 open-source software projects. Therefore, they trained
-different supervised machine learning classifiers to predict the
-root cause, impact, and software component.
-Thung et al. [18] used machine learning to classify bugs ac-
-cording to the Orthogonal Defect Classification (ODC) scheme.
-They distinguished three defect groups: data and control flow,
-structural, and non-functional. They manually classified 500
-bugs that serve as training set. They use the description of the
-bug as well as the fixes to train a model. The SVM multi-
-class classification algorithm performed best (69 % precision,
-70 % recall). Lopes and colleagues [23] applied different ML
-algorithms on bug descriptions to classify bugs according to
-different ODC dimensions. They manually categorized more
-than 4000 fixed bugs from three NoSQL databases. Recurrent
-Neural Networks have the highest accuracy when predicting
-the activity (47.6 %) and impact (33.3 %). Linear support vector
-machines are suited best to predict the target (accuracy 85.5 %)
-and the defect type (34.7 %).
-Hern ´andez-Gonz ´alez and colleagues [19] proposed a learning
-from crowds ML approach. The training data consists of bug
-reports and labels for OCD’s impact dimension. Each bug report
-was labeled by five annotators. In the majority of the cases, the
-annotators disagree on the labels. In the learning from crowds
-paradigm, the individual labels are taken in the machine learning
-training instead of the label that was assigned by the majority
-of the annotators.
-Antoniol et al. [20] use decision trees, naive Bayes and
-logistic regression to classify issue reports as bug or feature
-request. Their approach was able to correctly classify 77-82 %
-of the issues. Chawla and Singh [21] also classify issue reports
-as bug or other request. They receive an accuracy of 84-91 %.
-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000675VII. C ONCLUSION AND FUTURE WORK
-The presented approach automatically predicts the root cause
-for a given bug report. This information can be used by the
-developer to choose a proper debugging tool. It can also be
-used by a meta-debugging approach to recommend a debugging
-tool. The data set created in this work can be used to evaluate
-which debugging tools are particularly well-suited to support
-programmers in the debugging process of a particular bug
-instance. In addition, the proposed approach can be utilized
-for building benchmarks of specific bug types. This benchmark
-is especially suitable for evaluating IR-based fault localization
-techniques since it includes textual data in form of bug reports
-and commit messages, as well as detailed information on the
-fix location.
-During the manual classification, we have noticed recurring
-fault patterns. We will investigate if we can establish links be-
-tween these fault patterns and code-smells detected by existing
-code analysis tools such as SonarQube10. If so, knowledge about
-the bug type combined with reports from code analysis tools can
-be utilized to aid fault localization.
-Besides that, we will improve the approach by pre-processing
-stack trace information and other artifacts (if available in the bug
-report). Currently, stack trace information is treated the same
-way as human written text.
-Since a detailed predicted root cause is even more helpful,
-we will refine the prediction to the sub-categories. To do so, we
-have to enlarge the training set. Since certain subcategories will
-be underrepresented in the training set, we will up-sample those
-categories by means of the Synthetic Minority Over-sampling
-TEchnique (SMOTE) [24].
-ACKNOWLEDGMENT
-The work described in this paper has been funded by the
-Austrian Science Fund (FWF): P 32653 (Automated Debugging
-in Use).
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-©2020 IEEE, DOI: 10.1109/ISSREW51248.2020.000676

txt/2103.10051.txt

@@ -1,435 +0,0 @@
-© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any
-current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new
-collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other
-works.arXiv:2103.10051v1  [cs.LG]  18 Mar 2021DATA-FREE MIXED-PRECISION QUANTIZATION USING NOVEL SENSITIVITY METRIC
-Donghyun Lee, Minkyoung Cho, Seungwon Lee, Joonho Song, and Changkyu Choi
-Samsung Advanced Institute of Technology, Samsung Electronics, South Korea
-ABSTRACT
-Post-training quantization is a representative technique for
-compressing neural networks, making them smaller and more
-efficient for deployment on edge devices. However, an in-
-accessible user dataset often makes it difficult to ensure the
-quality of the quantized neural network in practice. In addi-
-tion, existing approaches may use a single uniform bit-width
-across the network, resulting in significant accuracy degrada-
-tion at extremely low bit-widths. To utilize multiple bit-width,
-sensitivity metric plays a key role in balancing accuracy and
-compression. In this paper, we propose a novel sensitivity
-metric that considers the effect of quantization error on task
-loss and interaction with other layers. Moreover, we develop
-labeled data generation methods that are not dependent on a
-specific operation of the neural network. Our experiments
-show that the proposed metric better represents quantization
-sensitivity, and generated data are more feasible to be applied
-to mixed-precision quantization.
-Index Terms —Deep Learning, Quantization, Data Free
-1. INTRODUCTION
-In recent years, deep neural networks have simplified and en-
-abled applications in numerous domains, especially for vision
-tasks [1, 2, 3]. Meanwhile, there is a need to minimize the
-memory footprint and reduce the network computation cost to
-deploy on edge devices. Significant efforts have been made to
-reduce the network size or accelerate inference of the neural
-network [4, 5, 6]. Several approaches exist to this problem,
-and quantization has been studied as one of the most reliable
-solutions. In the quantization process, low-bit representations
-of both weights and activations introduce quantization noise,
-which results in accuracy degradation. To alleviate the accu-
-racy loss, retraining or fine-tuning methods are developed by
-exploiting extensive training datasets [7, 8, 9].
-However, these methods are not applicable in many real-
-world scenarios, where the user dataset is inaccessible due to
-confidential or personal issues [10]. It is impossible to train
-the network and verify the quality of a quantized neural net-
-work without a user dataset. Although post-training quantiza-
-tion is a frequently suggested method to address this problem
-[11, 12], a small dataset is often required to decide the optimal
-Equal contribution
-(a) Data generation
- (b) Profiling for quantization
-(c) Sensitivity measurement
- (d) Mixed-precision inference
-Fig. 1 . Overall process of a post-training mixed-precision
-quantization method. (a) Generate dataset from pretrained
-network. (b) Post-training quantization using statistics. (c)
-Measure the sensitivity of each layer. (d) Quantize to higher
-precision for a more sensitive layer.
-clipping range of each layer. The credential statistics of lay-
-ers are important to ensure the performance of the quantized
-network in increasing task loss at ultra-low-bit precision.
-Considering that most quantization approaches have
-used uniform bit allocation across the network, the mixed-
-precision quantization approach takes a step further in pre-
-serving the accuracy by lifting the limit of those approaches
-[13, 14]. As a necessity, several sensitivity measurement
-methods for maximizing the effects of mixed-precision have
-also been proposed because it is difficult to determine which
-sections of the network are comparatively less susceptible
-to quantization [15, 16, 17]. To measure the quantization
-robustness of activations and weights, it is necessary to an-
-alyze the statistics generated during forward and backward
-processes by using a reliable dataset. The prior sensitivity
-metrics use the difference between outputs of the original
-model and the quantized model when quantizing each layer
-separately [10, 15]. However, this approach does not con-
-sider the interaction of quantization error and other layers.
-It cannot be neglected because a lower bit quantization im-
-plies a larger quantization error. Other prior studies require
-severe approximations to compute higher-order derivatives
-efficiently [16, 18].Fig. 2 . Top-2 confidences of each generated data sample on
-ResNet50. Confidence means the output values of the soft-
-max layer. For all classes except one (Class 744), the gener-
-ated labeled data samples pointed their target classes corre-
-sponding to the labels with the highest confidence.
-In this work, we provide a straightforward method to com-
-pute the layer-wise sensitivity for mixed-precision quantiza-
-tion, considering the interaction of quantization error. In addi-
-tion, we propose a data generation method, which is effective
-in the process of post-training mixed-precision quantization,
-as shown in Figure 1. Prior works [10, 19] use statistics of
-a particular operation (such as batch normalization), and it is
-impotent when the networks that do not have the specific op-
-eration explicitly. The proposed synthetic data engineering
-approach is independent of network structures. Moreover, it
-generates labeled data to verify the quantized network.
-The remainder of this paper is organized as follows. Sec-
-tion 2 describes the data generation method and proposes a
-sensitivity measurement metric. Section 3 provides an exper-
-imental demonstration of mixed-precision quantization using
-the proposed metric and generated data, and the results are
-compared with previous approaches.
-2. METHODOLOGY
-2.1. Data Generation
-For a general data generation method, we seek to avoid any
-dependency on a specific operation in a convolutional net-
-work. As shown in Figure 1(a), we first forward a noisy image
-initialized from a uniform distribution in the range between 0
-and 255, inclusive. To produce a set of labeled data, we use
-a set of class-relevant vectors/matrices, which means one-hot
-vectors per class in the classification task. In the forward pass,
-the loss is computed as
-x
-c= argmin
-xcLCE(f(y(xc)); vc) +
y(xc) (1)
-wherexcis the trainable input feature, vcis the set of class-
-relevant vectors/matrices, called the golden set, y(
)is theneural network, and f(
)is an activation function (i.e., soft-
-max) used to compute cross-entropy loss ( LCE) with the
-golden set. In addition, we reinforce the loss by maximizing
-the activation of output of network to enhance the efficiency
-of data generation process by referring the prior works for
-model interpretation [20, 21]. The calculated loss is prop-
-agated in the backward pass and generates input feature x
-c
-for each class, c2f1:::NumOfClass g. Finally, we have
-crafted synthetic data for each class after several iterations of
-forward and backward processes.
-Figure 2 demonstrates the reliability of crafting labeled
-data for each class by showing the top-2 confidences per class
-of synthetic data. All the generated data are used not only to
-measure the layer-wise sensitivity of the network but also to
-obtain the statistics of activations to improve the quality of the
-post-training quantized network.
-2.2. Sensitivity Metric
-The objective of mixed-precision quantization is to allocate
-appropriate bits to each layer to reduce the cost (e.g., bit op-
-erations [22]) of neural network models while suppressing the
-task loss growth. The sensitivity metric is an important factor
-for finding the optimal point between the effects of quanti-
-zation error and cost. First, we would like to measure the
-effect of quantization error on the loss of a network that has
-totalLlayers when weights of ithlayer (1iL),Wi
-are quantized through quantization function Qk(
)intok-bit.
-Given input data xand quantized neural network ^y, we con-
-sider the Euclidean distance between y(x)and^y(x)as theL,
-the loss of network output, for sensitivity measurement in-
-stead of task loss. We can define the effect of quantization
-errorQk(Wi)WiofQk(Wi)onLas follows:
-
-Wi(k) =1
-NX
-x
-@(Qk(Wi)Wi)
-whereNdenotes the total size of the dataset, and 
-Wi(k)are
-gradients for the quantization error. Wiis not variable in post-
-training quantization; thus, we can represent Eq. 2 as weight
-gradients of quantized parameters by using the chain rule as
-follows:
-@L
-@Qk(W)
@Qk(W)
-@(Qk(W)W)'@L
-@Qk(W)(3)
-The effects of the activation’s quantization error on Lis
-represented as activation gradients of quantized activation by
-applying the derivation of the formula shown in the previous
-equations. Subsequently, we calculate the expectation for the
-effects of the quantized network on Lby using the geometric
-mean of 
-ofm-bit quantized activation Qm(A)and quan-
-tized weights Qk(W), which is formulated asE[jS^yj] =LY
-i
-Ai(mi)
-Wi(ki)1
-L
-(4)
-The gradients of the converged single-precision neural
-network are not changed for the same data. To measure
-the effect of Qk(Wi)Wion other connected layers, we
-observe the gradient perturbations of WandA, which are
-caused byQk(Wi). Consequently, we can measure the effect
-of quantization error on other layers and loss of the network
-together, i.e., sensitivity of quantization by using E[jS^yj]
-when we only quantize activations or weights of a layer for
-which we would like to measure the sensitivity. Expressing
-the single-precision as QFP32(
), we have
-E[jSWjj] =LY
-i
-Ai(FP32)
- Wi(ki)1
-L
-(5)
-s:t: k i=(
-ki<32i=j
-FP32i6=j
-for quantization sensitivity metric for Wj. It is straightfor-
-ward by using the information of back-propagation of quan-
-tized network and considers the effect of quantization error
-on other layers.
-3. EXPERIMENTS
-In this section, we first demonstrate the effectiveness of the
-proposed data generation method in post-training quantiza-
-tion. Then, we show that the proposed sensitivity metric rep-
-resents the quantization sensitivity of the layer effectively by
-using our generated data. Finally, sensitivity value by using
-various datasets indicates that the proposed data generation
-method is also credible in sensitivity measurement.
-To demonstrate our methodology, we use classification
-models VGG19, ResNet50, and InceptionV3 on the ImageNet
-validation dataset.
-3.1. Efficacy of Data Generation Method
-We evaluate our method using the generated data to determine
-the clipping range of each layer in post-training quantization.
-Our experiments exploit a simple symmetric quantization ap-
-proach, which uses the maximum value of activation as the
-clipping range of each layer. Hence, maximum values are
-extremely crucial for confining the dynamic range to utilize
-the bit-width efficiently, preventing a severe accuracy drop in
-low-bit quantization.
-For our proposed method, input images are initialized ac-
-cording to uniform distribution, which follows standardiza-
-tion or normalization. To generate data, we use Adam op-
-timizer to optimize the loss function with a learning rate of
-0.04, and we found that =1 works best empirically.Model Dataset Top-1 Top-5
-VGG19ImageNet 72.31 90.81
-Noise 3.45 10.45
-ZeroQ - -
-Proposed 71.84 90.53
-ResNet50ImageNet 75.89 92.74
-Noise 11.28 29.88
-ZeroQ 75.47 92.62
-Proposed 75.68 92.72
-InceptionV3ImageNet 77.14 93.43
-Noise 62.49 85.00
-ZeroQ 76.85 93.31
-Proposed 76.83 93.26
-Table 1 . Results of post-training quantization (8-bit quantiza-
-tion for activations and weights) using different dataset. Ima-
-geNet presents the oracle performance in terms of using train-
-ing dataset and others are the results of the data-free methods.
-For InceptionV3, input data are initialized according to
-U(0, 255), which follows standardization with mean and vari-
-ance by considering that the model requires synthetic data to
-be converted into the range of [-1, 1] through preprocessing.
-For VGG19 and ResNet50, input data are initialized accord-
-ing toU(0, 255), which follows normalization with the factor
-of 255 because they require the range of [0, 1].
-Table 1 shows the empirical results for ImageNet, random
-noise, and ZeroQ [10]. In the experiment, ImageNet data are
-produced by choosing one image per class from the training
-data, and we generate 1000 data samples randomly using the
-existing method and the proposed method. As one can see,
-our method shows similar or higher performances over exist-
-ing data-free method, having less than 0.5% differences from
-the results of ImageNet cases. As shown in VGG19, although
-the existing research is hard to generalize, the method main-
-tains sound performance regardless of the network structure.
-3.2. Comparison of Sensitivity Metrics
-To verify several metrics in weight sensitivity, we measure
-the task loss by switching floating-point weights in the or-
-der of layers with higher sensitivity values, where all weights
-are quantized to 4-bit, and activations do not have quanti-
-zation error. We denote this experiment as A32W f32,4g.
-Af32,4gW32 indicates the evaluation of the metrics in acti-
-vation sensitivity when switching the 4-bit quantized activa-
-tion to floating-point, where weights are single-precision. Ze-
-roQ [10] measures the KL divergence and [15] measures the
-Euclidean distance between the original model and the quan-
-tized model. ZeroQ [10] only measures the weight sensitivity.
-HAWQ-V2 [16] uses the average Hessian trace and L2 norm
-of quantization perturbation. We use the data generated by
-the proposed method for all metrics.
-Figure 3 shows the results of ResNet50 and InceptionV3(a) ResNet50 A32W f32,4g
-(b) ResNet50 Af32, 4gW32
- (c) InceptionV3 A32W f32,4g
-(d) InceptionV3 Af32, 4gW32
-Fig. 3 . Results of sensitivity metric evaluation on ResNet50 and InceptionV3 over ImageNet dataset. A32W f32,4gis the
-evaluation for weight sensitivity and A f32,4gW32 is for activation sensitivity.
-Model Metric A32W f32,4gAf32,4gW32
-ResNet50Proposed 1 1
-L2 [15] 1.22 1.11
-ZeroQ(KLD) 1.14 18.48
-HAWQ-V2 1.42 2.09
-InceptionV3Proposed 1 1
-L2 1.04 1.50
-ZeroQ(KLD) 1.69 8.06
-HAWQ-V2 1.25 6.17
-Table 2 . Quantitative comparison of different sensitivity met-
-rics through relative area calculation of task loss graph.
-over ImageNet dataset. Our sensitivity metric reliably and
-quickly lowered the task loss, which means that the proposed
-sensitivity metric is good at weights and activations that are
-comparatively more susceptible to quantization. To express
-the results of Figure 3 quantitatively, we calculate the relative
-area under the curve of task loss graph considering the result
-of the proposed metric as 1 and summarize it in Table 2. Our
-proposed method has the smallest area in all results.
-3.3. Sensitivity According to Dataset Type
-To show the importance of the data in measuring the sensitiv-
-ity, we evaluate the proposed sensitivity metric over the differ-
-ent datasets of Section 3.1 on the 4-bit quantized network that
-clipped the activation range using ImageNet dataset. We mea-
-sure the task loss as in Section 3.2. The proposed dataset is the
-most similar in sensitivity to the ImageNet dataset, as shown
-in Table 3. Notably, the proposed data generation method pro-
-vides reliable statistics similar to the original training dataset.
-For InceptionV3, preprocessed ImageNet data are in the range
-of [-2.03, 2.52]. However, the range of the preprocessed data
-from [10] is measured as [-11.50, 11.21], while that of our
-data is in [-2.35, 2.36], whose maximum value is almost simi-
-lar to that of the ImageNet dataset. These similar statistics are
-seen in ResNet50. The incorrect statistics of the activation
-data corresponding to the original training dataset implies in-Model Dataset A32W f32,4gAf32,4gW32
-ResNet50Proposed 1 1
-ImageNet 0.74 1.60
-Noise 1.35 6.76
-ZeroQ 1.11 3.17
-InceptionV3Proposed 1 1
-ImageNet 1.07 2.48
-Noise 1.36 3.54
-ZeroQ 1.47 2.63
-Table 3 . Quantitative comparison of using different datasets
-in sensitivity measurement through relative area calculation
-of task loss graph.
-accurate sensitivity measurement [10]. Thus, our generation
-method can be used for reliable sensitivity measurement.
-4. CONCLUSION
-In this paper, we proposed an effective data generation
-method for post-training mixed-precision quantization. Our
-approach is to train the random noise to generate data by using
-class-relevant vectors. It is not only independent of network
-structure but also provides a labeled dataset. We demonstrate
-that the generated data are sufficient to ensure the quality of
-post-training quantization. Furthermore, we proposed a novel
-sensitivity metric, which is important to optimize bit alloca-
-tion. The proposed sensitivity metric considers the effect of
-quantization on other relative layers and task loss together us-
-ing the gradient perturbation of the quantized neural network.
-Comparisons of sensitivity metrics were made to show the
-extent to which a layer with high sensitivity, measured with
-sensitivity metrics of other methods, affects task loss. The
-proposed sensitivity metric outperformed other metrics to
-stand for the effect of quantization error. We leave optimizing
-bit allocation using the proposed metric and applying to other
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txt/2103.13922.txt

@@ -1,1473 +0,0 @@
-ScanGAN360: A Generative Model of Realistic Scanpaths for 360Images
-Daniel Martin1Ana Serrano2Alexander W. Bergman3Gordon Wetzstein3
-Belen Masia1
-1Universidad de Zaragoza, I3A2Centro Universitario de la Defensa, Zaragoza3Stanford University
-Abstract
-Understanding and modeling the dynamics of human
-gaze behavior in 360environments is a key challenge in
-computer vision and virtual reality. Generative adversar-
-ial approaches could alleviate this challenge by generat-
-ing a large number of possible scanpaths for unseen im-
-ages. Existing methods for scanpath generation, however,
-do not adequately predict realistic scanpaths for 360im-
-ages. We present ScanGAN360, a new generative adver-
-sarial approach to address this challenging problem. Our
-network generator is tailored to the specifics of 360im-
-ages representing immersive environments. Specifically, we
-accomplish this by leveraging the use of a spherical adapta-
-tion of dynamic-time warping as a loss function and propos-
-ing a novel parameterization of 360scanpaths. The quality
-of our scanpaths outperforms competing approaches by a
-large margin and is almost on par with the human baseline.
-ScanGAN360 thus allows fast simulation of large numbers
-ofvirtual observers , whose behavior mimics real users, en-
-abling a better understanding of gaze behavior and novel
-applications in virtual scene design.
-1. Introduction
-Virtual reality (VR) is an emerging medium that unlocks
-unprecedented user experiences. To optimize these expe-
-riences, however, it is crucial to develop computer vision
-techniques that help us understand how people explore im-
-mersive virtual environments. Models for time-dependent
-visual exploration behavior are important for designing and
-editing VR content [42], for generating realistic gaze trajec-
-tories of digital avatars [18], for understanding dynamic vi-
-sual attention and visual search behavior [60], and for devel-
-oping new rendering, display, and compression algorithms,
-among other applications.
-Current approaches that model how people explore vir-
-tual environments often leverage saliency prediction [43,
-13, 31, 2]. While this is useful for some applications, the
-fixation points predicted by these approaches do not account
-Figure 1. We present ScanGAN360, a generative adversarial ap-
-proach to scanpath generation for 360images. ScanGAN360
-generates realistic scanpaths ( bottom rows ), outperforming state-
-of-the-art methods and mimicking the human baseline ( top row ).
-for the time-dependent visual behavior of the user, making
-it difficult to predict the order of fixations, or give insight
-into how people explore an environment over time. For this
-purpose, some recent work has explored scanpath predic-
-tion [2, 3, 62, 4], but these algorithms do not adequately
-model how people explore immersive virtual environments,
-resulting in erratic or non-plausible scanpaths.
-In this work, we present ScanGAN360, a novel frame-
-work for scanpath generation for 360images (Figure 1).
-Our model builds on a conditional generative adversarial
-network (cGAN) architecture, for which we discuss and val-
-idate two important insights that we show are necessary for
-realistic scanpath generation. First, we propose a loss func-
-tion based on a spherical adaptation of dynamic time warp-
-ing (DTW), which is a key aspect for training our GAN ro-
-bustly. DTW is a metric for measuring similarity between
-two time series, such as scanpaths, which to our knowledge
-has not been used to train scanpath-generating GANs. Sec-
-ond, to adequately tackle the problem of scanpath genera-
-tion in 360images, we present a novel parameterization ofarXiv:2103.13922v1  [cs.CV]  25 Mar 2021the scanpaths. These insights allow us to demonstrate state-
-of-the-art results for scanpath generation in VR, close to the
-human baseline and far surpassing the performance of ex-
-isting methods. Our approach is the first to enable robust
-scanpath prediction over long time periods up to 30 sec-
-onds, and, unlike previous work, our model does not rely
-on saliency, which is typically not available as ground truth.
-Our model produces about 1,000 scanpaths per second,
-which enables fast simulation of large numbers of virtual
-observers , whose behavior mimics that of real users. Us-
-ing ScanGAN360, we explore applications in virtual scene
-design, which is useful in video games, interior design,
-cinematography, and tourism, and scanpath-driven video
-thumbnail generation of 360images, which provides pre-
-views of VR content for social media platforms. Beyond
-these applications, we propose to use ScanGAN360 for
-applications such as gaze behavior simulation for virtual
-avatars or gaze-contingent rendering. Extended discussion
-and results on applications are included in the supplemen-
-tary material and video.
-We will make our source code and pre-trained model
-publicly available to promote future research.
-2. Related work
-Modeling and predicting attention The multimodal na-
-ture of attention [30], together with the complexity of hu-
-man gaze behavior, make this a very challenging task. Many
-works devoted to it have relied on representations such as
-saliency, which is a convenient representation for indicat-
-ing the regions of an image more likely to attract atten-
-tion. Early strategies for saliency modeling have focused
-on either creating hand-crafted features representative of
-saliency [19, 52, 61, 29, 20, 7], or directly learning data-
-driven features [49, 22]. With the proliferation of exten-
-sive datasets of human attention [43, 39, 20, 8, 59], deep
-learning–based methods for saliency prediction have been
-successfully applied, yielding impressive results [37, 36, 14,
-50, 54, 55, 58].
-However, saliency models do not take into account the
-dynamic nature of human gaze behavior, and therefore, they
-are unable to model or predict time-varying aspects of at-
-tention. Being able to model and predict dynamic explo-
-ration patterns has been proven to be useful, for example,
-for avatar gaze control [12, 41], video rendering in virtual
-reality [26], or for directing users’ attention over time in
-many contexts [9, 38]. Scanpath models aim to predict vi-
-sual patterns of exploration that an observer would perform
-when presented with an image. In contrast to saliency mod-
-els, scanpath models typically focus on predicting plausi-
-ble scanpaths, i.e., they do not predict a unique scanpath
-and instead they try to mimic human behavior when ex-
-ploring an image, taking into account the variability be-
-tween different observers. Ellis and Smith [16] were pio-neers in this field: they proposed a general framework for
-generating scanpaths based on Markov stochastic processes.
-Several approaches have followed this work, incorporating
-behavioral biases in the process in order to produce more
-plausible scanpaths [24, 47, 27, 48]. In recent years, deep
-learning models have been used to predict human scanpaths
-based on neural network features trained on object recogni-
-tion [22, 53, 14, 5].
-Attention in 360images Predicting plausible scanpaths
-in 360imagery is a more complex task: Observers do not
-only scan a given image with their gaze, but they can now
-also turn their head or body, effectively changing their view-
-port over time. Several works have been proposed for mod-
-eling saliency in 360images [33, 43, 31, 11, 44]. However,
-scanpath prediction has received less attention. In their re-
-cent work, Assens et al. [3] generalize their 2D model to
-360images, but their loss function is unable to reproduce
-the behavior of ground truth scanpaths (see Figure 4, third
-column). A few works have focused on predicting short-
-term sequential gaze points based on users’ previous his-
-tory for 360videos, but they are limited to small temporal
-windows (from one to ten seconds) [56, 25, 35]. For the
-case of images, a number of recent methods focus on devel-
-oping improved saliency models and principled methods to
-sample from them [2, 4, 62].
-Instead, we directly learn dynamic aspects of attention
-from ground truth scanpaths by training a generative model
-in an adversarial manner, with an architecture and loss
-function specifically designed for scanpaths in 360im-
-ages. This allows us to (i) effectively mimic human be-
-havior when exploring scenes, bypassing the saliency gen-
-eration and sampling steps, and (ii) optimize our network to
-stochastically generate 360scanpaths, taking into account
-observer variability.
-3. Our Model
-We adopt a generative adversarial approach, specifically
-designed for 360content in which the model learns to gen-
-erate a plausible scanpath, given the 360image as a con-
-dition. In the following, we describe the parameterization
-employed for the scanpaths, the design of our loss function
-for the generator, and the particularities of our conditional
-GAN architecture, ending with details about the training
-process.
-3.1. Scanpath Parameterization
-Scanpaths are commonly provided as a sequence of two-
-dimensional values corresponding to the coordinates (i;j)
-of each gaze point in the image. When dealing with 360
-images in equirectangular projections, gaze points are also
-often represented by their latitude and longitude (;),Figure 2. Illustration of our generator and discriminator networks. Both networks have a two-branch structure: Features extracted from the
-360image with the aid of a CoordConv layer and an encoder-like network are concatenated with the input vector for further processing.
-The generator learns to transform this input vector, conditioned by the image, into a plausible scanpath. The discriminator takes as input
-vector a scanpath (either captured or synthesized by the generator), as well as the corresponding image, and determines the probability of
-this scanpath being real (or fake). We train them end-to-end in an adversarial manner, following a conditional GAN scheme. Please refer
-to the text for details on the loss functions and architecture.
-2[
-2;
-2]and2[;]. However, these parame-
-terizations either suffer from discontinuities at the borders
-of a 360image, or result in periodic, ambiguous values.
-The same point of the scene can have two different repre-
-sentations in these parameterizations, hindering the learning
-process.
-We therefore resort to a three-dimensional parameteriza-
-tion of our scanpaths, where each gaze point p= (;)is
-transformed into its three-dimensional representation P=
-(x;y;z )such that:
-x=cos()cos();y=cos()sin();z=sin():
-This transformation assumes, without loss of generality,
-that the panorama is projected over a unit sphere. We
-use this parameterization for our model, which learns a
-scanpath Pas a set of three-dimensional points over time.
-Specifically, given a number of samples Tover time, P=
-(P1;:::;PT)2R3T. The results of the model are then
-converted back to a two-dimensional parameterization in
-terms of latitude ( =atan2 (z;p
-x2+y2)) and longitude
-(=atan2 (y;x)) for display and evaluation purposes.
-3.2. Overview of the Model
-Our model is a conditional GAN, where the condition
-is the RGB 360image for which we wish to estimate a
-scanpath. The generator Gis trained to generate a scanpath
-from a latent code z(drawn randomly from a uniform distri-
-bution,U(1;1)), conditioned by the RGB 360imagey.
-The discriminator Dtakes as input a potential scanpath ( xorG(z;y)), as well as the condition y(the RGB 360im-
-age), and outputs the probability of the scanpath being real
-(or fake). The architecture of both networks, generator and
-discriminator, can be seen in Figure 2, and further details
-related to the architecture are described in Section 3.4.
-3.3. Loss Function
-The objective function of a conventional conditional
-GAN is inspired by a minimax objective from game theory,
-with an objective [32]:
-min
-Gmax
-DV(D;G ) =
-Ex[logD(x;y)] +Ez[log(1D(G(z;y);y)]:(1)
-We can separate this into two losses, one for the generator,
-LG, and one for the discriminator, LD:
-LG=Ez[log(1D(G(z;y);y))]; (2)
-LD=Ex[logD(x;y)] +Ez[log(1D(G(z;y);y))]:(3)
-While this objective function suffices in certain cases, as
-the complexity of the problem increases, the generator may
-not be able to learn the transformation from the input distri-
-bution into the target one. One can resort to adding a loss
-term toLG, and in particular one that enforces similarity to
-the scanpath ground truth data. However, using a conven-
-tional data term, such as MSE, does not yield good results
-(Section 4.4 includes an evaluation of this). To address this
-issue, we introduce a novel term in LGspecifically targeted
-to our problem, and based on dynamic time warping [34].Dynamic time warping (DTW) measures the similar-
-ity between two temporal sequences, considering both the
-shape and the order of the elements of a sequence, with-
-out forcing a one-to-one correspondence between elements
-of the time series. For this purpose, it takes into account
-all the possible alignments of two time series rands, and
-computes the one that yields the minimal distance between
-them. Specifically, the DTW loss function between two
-time series r2Rknands2Rkmcan be expressed
-as [15]:
-DTW (r;s) = min
-AhA;
(r;s)i; (4)
-where 
(r;s) = [(ri;sj)]ij2Rnmis a matrix con-
-taining the distances (
;
)between each pair of points in r
-ands,Ais a binary matrix that accounts for the alignment
-(or correspondence) between rands, andh
;
iis the inner
-product between both matrices.
-In our case, r= (r1;:::;rT)2R3Tands=
-(s1;:::;sT)2R3Tare two scanpaths that we wish to com-
-pare. While the Euclidean distance between each pair of
-points is usually employed when computing (ri;sj)for
-Equation 4, in our scenario that would yield erroneous dis-
-tances derived from the projection of the 360image (both
-if done in 2D over the image, or in 3D with the parameteri-
-zation described in Section 3.1). We instead use the distance
-over the surface of a sphere, or spherical distance, and de-
-fine
sph(r;s) = [sph(ri;sj)]ij2Rnmsuch that:
-sph(ri;sj) =
-2 arcsin1
-2q
-(rx
-isx
-j)2+ (ry
-isy
-j)2+ (rz
-isz
-j)2
-;
-(5)
-leading to our spherical DTW:
-DTWsph(r;s) = min
-AhA;
sph(r;s)i: (6)
-We incorporate the spherical DTW to the loss function of
-the generator (LG, Equation 2), yielding our final generator
-loss functionL
-G:
-L
-G=LG+
Ez[DTWsph(G(z;y);)]; (7)
-whereis a ground truth scanpath for the conditioning im-
-agey, and the weight is empirically set to 0:1.
-While a loss function incorporating DTW (or spherical
-DTW) is not differentiable, a differentiable version, soft-
-DTW, has been proposed. We use this soft-DTW in our
-model; details on it can be found in Section S1 in the sup-
-plementary material or in the original publication [15].
-3.4. Model Architecture
-Both our generator and discriminator are based on a two-
-branch structure (see Figure 2), with one branch for the con-
-ditioning image yand the other for the input vector ( zin thegenerator, and xorG(z;y)in the discriminator). The im-
-age branch extracts features from the 360image, yielding
-a set of latent features that will be concatenated with the
-input vector for further processing. Due to the distortion
-inherent to equirectangular projections, traditional convo-
-lutional feature extraction strategies are not well suited for
-360images: They use a kernel window where neighboring
-relations are established uniformly around a pixel. Instead,
-we extract features using panoramic (or spherical) convolu-
-tions [13]. Spherical convolutions are a type of dilated con-
-volutions where the relations between elements in the im-
-age are not established in image space, but in a gnomonic,
-non-distorted space. These spherical convolutions can rep-
-resent kernels as patches tangent to a sphere where the 360
-is reprojected.
-In our problem of scanpath generation, the location of
-the features in the image is of particular importance. There-
-fore, to facilitate spatial learning of the network, we use the
-recently presented CoordConv strategy [28], which gives
-convolutions access to its own input coordinates by adding
-extra coordinate channels. We do this by concatenating a
-CoordConv layer to the input 360image (see Figure 2).
-This layer also helps stabilize the training process, as shown
-in Section 4.4.
-3.5. Dataset and Training Details
-We train our model using Sitzmann et al.’s [43] dataset,
-composed of 22 different 360images and a total of 1,980
-scanpaths from 169 different users. Each scanpath contains
-gaze information captured during 30 seconds with a binoc-
-ular eye tracking recorder at 120 Hz. We sample these cap-
-tured scanpaths at 1 Hz ( i.e.,T= 30 ), and reparameter-
-ize them (Section 3.1), so that each scanpath is a sequence
-P= (P0;:::;P 29)2R3T. Given the relatively small size
-of the dataset, we perform data augmentation by longitu-
-dinally shifting the 360images (and adjusting their scan-
-paths accordingly); specifically, for each image we generate
-six different variations with random longitudinal shifting.
-We use 19 of the 22 images in this dataset for training, and
-reserve three to be part of our test set (more details on the
-full test set are described in Section 4). With the data aug-
-mentation process, this yields 114 images in the training set.
-During our training process we use the Adam opti-
-mizer [21], with constant learning rates lG= 104for the
-generator, and lD= 105for the discriminator, both of
-them with momentum = (0:5;0:99). Further training and
-implementation details can be found in the supplementary
-material.
-4. Validation and Analysis
-We evaluate the quality of the generated scanpaths with
-respect to the measured, ground truth scanpaths, as well asFigure 3. Results of our model for two different scenes: market andmall from Rai et al.’s dataset [39]. From left to right : 360image,
-ground truth sample scanpath, and three scanpaths generated by our model. The generated scanpaths are plausible and focus on relevant
-parts of the scene, yet they exhibit the diversity expected among different human observers. Please refer to the supplementary material for
-a larger set of results.
-to other approaches. We also ablate our model to illustrate
-the contribution of the different design choices.
-We evaluate or model on two different test sets. First,
-using the three images from Sitzmann et al.’s dataset [43]
-left out of the training (Section 3.5): room ,chess androbots .
-To ensure our model has an ability to extrapolate, we also
-evaluate it with a different dataset from Rai et al. [39]. This
-dataset consists of 60 scenes watched by 40 to 42 observers
-for 25 seconds. Thus, when comparing to their ground truth,
-we cut our 30-second scanpaths to the maximum length of
-their data. Please also refer to the supplementary material
-for more details on the test set, as well as further evaluation
-and results.
-4.1. Scanpath Similarity Metrics
-Our evaluation is both quantitative and qualitative. Eval-
-uating scanpath similarity is not a trivial task, and a num-
-ber of metrics have been proposed in the literature, each fo-
-cused on a different context or aspect of gaze behavior [17].
-Proposed metrics can be roughly categorized into: (i) di-
-rect measures based on Euclidean distance; (ii) string-based
-measures based on string alignment techniques (such as the
-Levenshtein distance, LEV); (iii) curve similarity methods;
-(iv) metrics from time-series analysis (like DTW, on which
-our loss function is based); and (v) metrics from recurrence
-analysis ( e.g., recurrence measure REC and determinism
-measure DET). We refer the reader to supplementary mate-
-rial and the review by Fahimi and Bruce [17] for an in-depth
-explanation and comparison of existing metrics. Here, we
-include a subset of metrics that take into account both the
-position and the ordering of the points (namely LEV and
-DTW), and two metrics from recurrence analysis (REC and
-DET), which have been reported to be discriminative in
-revealing viewing behaviors and patterns when comparing
-scanpaths. We nevertheless compute our evaluation for the
-full set of metrics reviewed by Fahimi and Bruce [17] in the
-supplementary material.
-Since for each image we have a number of ground truthscanpaths, and a set of generated scanpaths, we compute
-each similarity metric for all possible pairwise comparisons
-(each generated scanpath against each of the ground truth
-scanpaths), and average the result. In order to provide an
-upper baseline for each metric, we also compute the human
-baseline ( Human BL ) [57], which is obtained by comparing
-each ground truth scanpath against all the other ground truth
-ones, and averaging the results. In a similar fashion, we
-compute a lower baseline based on sampling gaze points
-randomly over the image ( Random BL ).
-4.2. Results
-Qualitative results of our model can be seen in Figures 3
-and 1 for scenes with different layouts. Figure 3, from left
-to right, shows: the scene, a sample ground truth (captured)
-scanpath, and three of our generated scanpaths sampled
-from the generator. Our model is able to produce plausible,
-coherent scanpaths that focus on relevant parts of the scene.
-In the generated scanpaths we observe regions where the
-user focuses (points of a similar color clustered together), as
-well as more exploratory behavior. The generated scanpaths
-are diverse but plausible, as one would expect if different
-users watched the scene (the supplementary material con-
-tains more ground truth, measured scanpaths, showing this
-diversity). Further, our model is not affected by the inherent
-distortions of the 360image. This is apparent, for exam-
-ple, in the market scene: The central corridor, narrow and
-seemingly featureless, is observed by generated virtual ob-
-servers . Quantitative results in Table 1 further show that our
-generated scanpaths are close to the human baseline ( Hu-
-man BL ), both in the test set from Sitzmann et al.’s dataset,
-and over Rai et al.’s dataset. A value close to Human BL in-
-dicates that the generated scanpaths are as valid or as plau-
-sible as the captured, ground truth ones. Note that obtaining
-a value lower than Human BL is possible, if the generated
-scanpaths are on average closer to the ground truth ones,
-and exhibit less variance.
-Since our model is generative, it can generate as manyFigure 4. Qualitative comparison to previous methods for five different scenes from Rai et al.’s dataset. In each row, from left to right:
-360image, and a sample scanpath obtained with our method, PathGAN [3], SaltiNet [4], and Zhu et al.’s [62]. Note that, in the case
-of PathGAN, we are including the results directly taken from their paper, thus the different visualization. Our method produces plausible
-scanpaths focused on meaningful regions, in comparison with other techniques. Please see text for details, and the supplementary material
-for a larger set of results, also including ground truth scanpaths.
-scanpaths as needed and model many different potential ob-
-servers. We perform our evaluations on a random set of 100
-scanpaths generated by our model. We choose this num-
-ber to match the number of generated scanpaths available
-for competing methods, to perform a fair comparison. Nev-
-ertheless, we have analyzed the stability of our generative
-model by computing our evaluation metrics for a variable
-number of generated scanpaths: Our results are very sta-
-ble with the number of scanpaths (please see Table 2 in the
-supplementary material).
-4.3. Comparison to Other Methods
-We compare ScanGAN360 to three methods devoted to
-scanpath prediction in 360images: SaltiNet-based scan-
-path prediction [2, 4] (we will refer to it as SaltiNet in the
-following), PathGAN [3] and Zhu et al.’s method [62]. For
-comparisons to SaltiNet we use the public implementation
-of the authors, while the authors of Zhu et al. kindly pro-
-vided us with the results of their method for the images from
-Rai et al.’s dataset (but not for Sitzmann et al.’s); we there-
-fore have both qualitative (Figure 4) and quantitative (Ta-
-ble 1) comparisons to these two methods. In the case of
-PathGAN, no model or implementation could be obtained,
-so we compare qualitatively to the results extracted from
-their paper (Figure 4, third column).
-Table 1 shows that our model consistently provides re-sults closer to the ground truth scanpaths than Zhu et al.’s
-and SaltiNet. The latter is based on a saliency-sampling
-strategy, and thus these results indicate that indeed the tem-
-poral information learnt by our model is relevant for the fi-
-nal result. Our model, as expected, also amply surpasses the
-random baseline. In Figure 4 we see how PathGAN scan-
-paths fail to focus on the relevant parts of the scene (see,
-e.g.,snow orsquare ), while SaltiNet exhibits a somewhat
-erratic behavior, with large displacements and scarce areas
-of focus ( train ,snow orsquare show this). Finally, Zhu
-et al.’s approach tends to place gaze points at high contrast
-borders (see, e.g.,square orresort ).
-4.4. Ablation Studies
-We also evaluate the contribution of different elements of
-our model to the final result. For this purpose, we analyze
-a standard GAN strategy ( i.e., using only the discriminative
-loss), as the baseline. Figure 5 shows how the model is un-
-able to learn both the temporal nature of the scanpaths, and
-their relation to image features. We also analyze the results
-yielded by adding a term based on the MSE between the
-ground truth and the generated scanpath to the loss function,
-instead of our DTW sphterm (the only previous GAN ap-
-proach for scanpath generation [3] relied on MSE for their
-loss term). The MSE only measures a one-to-one corre-
-spondence between points, considering for each time instantFigure 5. Qualitative ablation results. From top to bottom : ba-
-sic GAN strategy (baseline); adding MSE to the loss function of
-the former; our approach; and an example ground truth scanpath.
-These results illustrate the need for our DTW sphloss term.
-Table 1. Quantitative comparisons of our model against
-SaltiNet [4] and Zhu et al. [62]. We also include upper (human
-baseline, Human BL ) and lower (randomly sampling over the im-
-age, Random BL ) baselines. Arrows indicate whether higher or
-lower is better, and boldface highlights the best result for each met-
-ric (excluding the ground truth Human BL ).SaltiNet is trained
-with Rai et al.’s dataset; we include it for completeness.
-Dataset Method LEV#DTW#REC"DET"
-Test set from
-Sitzmann et al.Random BL 52.33 2370.56 0.47 0.93
-SaltiNet 48.00 1928.85 1.45 1.78
-ScanGAN360 (ours) 46.15 1921.95 4.82 2.32
-Human BL 43.11 1843.72 7.81 4.07
-Rai et al.’s
-datasetRandom BL 43.11 1659.75 0.21 0.94
-SaltiNet48.07 1928.41 1.43 1.81
-Zhu et al. 43.55 1744.20 1.64 1.50
-ScanGAN360 (ours) 40.99 1549.59 1.72 1.87
-Human BL 39.59 1495.55 2.33 2.31
-a single point, unrelated to the rest. This hinders the learn-
-ing process, leading to non-plausible results (Figure 5, sec-
-ond row). This behavior is corrected when our DTW sphis
-added instead, since it is specifically targeted for time series
-data and takes into account the actual spatial structure of the
-data (Figure 5, third row). The corresponding quantitative
-measures over our test set from Sitzmann et al. can be found
-in Table 2. We also analyze the effect of removing the Co-
-ordConv layer from our model: Results in Table 2 indicate
-that the use of CoordConv does have a positive effect on the
-results, helping learn the transformation from the input to
-the target domain.Table 2. Quantitative results of our ablation study. Arrows indi-
-cate whether higher or lower is better, and boldface highlights the
-best result for each metric (excluding the ground truth Human BL ).
-Please refer to the text for details on the ablated models.
-Metric LEV# DTW# REC"DET"
-Basic GAN 49.42 2088.44 3.01 1.74
-MSE 48.90 1953.21 2.41 1.73
-DTWsph(no CoordConv) 47.82 1988.38 3.67 1.99
-DTWsph(ours) 46.19 1925.20 4.50 2.33
-Human Baseline (Human BL) 43.11 1843.72 7.81 4.07
-4.5. Behavioral Evaluation
-While the previous subsections employ well-known met-
-rics from the literature to analyze the performance of our
-model, in this subsection we perform a higher-level analysis
-of its results. We assess whether the behavioral characteris-
-tics of our scanpaths match those which have been reported
-from actual users watching 360images.
-Exploration time Sitzmann et al. [43] measure the explo-
-ration time as the average time that users took to move their
-eyes to a certain longitude relative to their starting point,
-and measure how long it takes for users to fully explore the
-scene. Figure 6 (left) shows this exploration time, measured
-by Sitzmann et al. from captured data, for the three scenes
-from their dataset included in our test set ( room ,chess , and
-robots ). To analyze whether our generated scanpaths mimic
-this behavior and exploration speed, we plot the exploration
-time of our generated scanpaths (Figure 6, center left) for
-the same scenes and number of scanpaths. We can see how
-the speed and exploration time are very similar between
-real and generated data. Individual results per scene can
-be found in the supplementary material.
-Fixation bias Similar to the center bias of human eye fix-
-ations observed in regular images [20], the existence of a
-Laplacian-like equator bias has been measured in 360im-
-ages [43]: The majority of fixations fall around the equa-
-tor, in detriment of the poles. We have evaluated whether
-the distribution of scanpaths generated by our model also
-presents this bias. This is to be expected, since the data our
-model is trained with exhibits it, but is yet another indicator
-that we have succeeded in learning the ground truth distri-
-bution. We test this by generating, for each scene, 1,000
-different scanpaths with our model, and aggregating them
-over time to produce a pseudo- saliency map, which we term
-aggregate map . Figure 6 (right) shows this for two scenes
-in our test set: We can see how this equator bias is indeed
-present in our generated scanpaths.
-Inter-observer congruency It is common in the literature
-analyzing users’ gaze behavior to measure inter-observerFigure 6. Behavioral evaluation. Left: Exploration time for real captured data ( left) and scanpaths generated by our model ( center left ).
-Speed and exploration time of our scanpaths are on par with that of real users. Center right : ROC curve of our generated scanpaths for
-each individual test scene (gray), and averaged across scenes (magenta). The faster it converges to the maximum rate, the higher the
-inter-observer congruency. Right : Aggregate maps for two different scenes, computed as heatmaps from 1,000 generated scanpaths. Our
-model is able to produce aggregate maps that focus on relevant areas of the scenes and exhibit the equator bias reported in the literature.
-congruency, often by means of a receiver operating char-
-acteristic (ROC) curve. We compute the congruency of our
-“generated observers” through this ROC curve for the three
-scenes in our test set from the Sitzmann et al. dataset (Fig-
-ure 6, center right). The curve calculates the ability of the
-ithscanpath to predict the aggregate map of the correspond-
-ing scene. Each point in the curve is computed by gener-
-ating a map containing the top n%most salient regions of
-the aggregate map (computed without the ithscanpath), and
-calculating the percentage of gaze points of the ithscanpath
-that fall into that map. Our ROC curve indicates a strong
-agreement between our scanpaths, with around 75% of all
-gaze points falling within 25% of the most salient regions.
-These values are comparable to those measured in previous
-studies with captured gaze data [43, 23].
-Temporal and spatial coherence Our generated scan-
-paths have a degree of stochasticity, to be able to model the
-diversity of real human observers. However, human gaze
-behavior follows specific patterns, and each gaze point is
-conditioned not only by the features in the scene but also by
-the previous history of gaze points of the user. If two users
-start watching a scene in the same region, a certain degree
-of coherence between their scanpaths is expected, that may
-diverge more as more time passes. We analyze the temporal
-coherence of generated scanpaths that start in the same re-
-gion, and observe that indeed our generated scanpaths fol-
-low a coherent pattern. Please refer to the supplementary
-for more information on this part of the analysis.
-5. Conclusion
-In summary, we propose ScanGAN360, a conditional
-GAN approach to generating gaze scanpaths for immersive
-virtual environments. Our unique parameterization tailored
-to panoramic content, coupled with our novel usage of a
-DTW loss function, allow our model to generate scanpaths
-of significantly higher quality and duration than previousapproaches. We further explore applications of our model:
-Please refer to the supplementary material for a description
-and examples of these.
-Our GAN approach is well suited for the problem of
-scanpath generation: A single ground truth scanpath does
-not exist, yet real scanpaths follow certain patterns that
-are difficult to model explicitly but that are automatically
-learned by our approach. Note that our model is also very
-fast and can produce about 1,000 scanpaths per second.
-This may be a crucial capability for interactive applications:
-our model can generate virtual observers in real time.
-Limitations and future work Our model is trained with
-30-second long scanpaths, sampled at 1 Hz. Although
-this is significantly longer than most previous approaches
-[16, 23, 27], exploring different or variable lengths or sam-
-pling rates remains interesting for future work. When train-
-ing our model, we focus on learning higher-level aspects of
-visual behavior, and we do not explicitly enforce low-level
-ocular movements ( e.g., fixations or saccades). Currently,
-our relatively low sampling rate prevents us from model-
-ing very fast dynamic phenomena, such as saccades. Yet,
-fixation patterns naturally emerge in our results, and future
-work could explicitly take low-level oculomotor aspects of
-visual search into account.
-The model, parameterization, and loss function are tai-
-lored to 360images. In a similar spirit, a DTW-based loss
-function could also be applied to conventional 2D images
-(using an Euclidean distance in 2D instead of our sph), po-
-tentially leading to better results than current 2D approaches
-based on mean-squared error.
-We believe that our work is a timely effort and a first step
-towards understanding and modeling dynamic aspects of at-
-tention in 360images. We hope that our work will serve
-as a basis to advance this research, both in virtual reality
-and in conventional imagery, and extend it to other scenar-
-ios, such as dynamic or interactive content, analyzing the
-influence of the task, including the presence of motion par-allax, or exploring multimodal experiences. We will make
-our model and training code available in order to facilitate
-the exploration of these and other possibilities.
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-1, 2, 6, 7, 4Supplementary Material
-This document offers additional information and details
-on the following topics:
-• (S1) Extended description of the soft-DTW (differen-
-tiable version of DTW) distance metric used in our
-model.
-• (S2) Additional results (scanpaths generated with our
-method) for different scenes used in our evaluation in
-the main paper.
-• (S3) Additional ground truth scanpaths for the scenes
-used in our evaluation in the main paper.
-• (S4) Further details on our training process.
-• (S5) Further details on metrics and evaluation, includ-
-ing a larger set of metrics (which we briefly introduce),
-and extended analysis.
-• (S6) Further details on the behavioral evaluation of our
-scanpaths.
-• (S7) Example applications of our method.
-S1. Differentiable Dynamic Time Warping:
-soft-DTW
-One of the key aspects of our framework relies in the
-addition of a second term to the generator’s loss function,
-based on dynamic time warping [34]. As pointed in Section
-3.3 in the main paper, dynamic time warping (DTW) mea-
-sures the similarity between two temporal sequences (see
-Figure 71, Equation 5 in the main paper for the original
-DTW formulation, and Equations 6 and 7 in the main pa-
-per for our spherical modification on DTW). However, the
-original DTW function is not differentiable, therefore it is
-not suitable as a loss function. Instead, we use a differen-
-tiable version of it, soft-DTW, which has been recently pro-
-posed [15] and used as a loss function in different problems
-dealing with time series [6, 10, 51].
-Differently from the original DTW formulation (Equa-
-tion 5 in the main paper), the soft-DTW is defined as fol-
-lows:
-soft-DTW
-A
-where, as with traditional DTW, 
(r;s) = [(ri;sj]ij2
-Rnmis a matrix containing the distances (
;
)between
-each pair of points in rands,Ais a binary matrix that
-accounts for the alignment (or correspondence) between r
-ands, andh
;
iis the inner product between both matrices.
-1https://databricks.com/blog/2019/04/30/understanding-dynamic-
-time-warping.html
-Figure 7. Simple visualization of dynamic time warping (DTW)
-alignment. Instead of assuming a pair-wise strict correspondence,
-DTW optimizes the alignment between two sequences to minimize
-their distance.
-In our case, r= (r1;:::;rT)2R3Tands= (s1;:::;sT)2
-R3Tare two scanpaths that we wish to compare.
-The main difference lies in the replacement of the minA
-with the minA
-min
 = 0
-
-neai=
 > 0(9)
-This soft-min function allows DTW to be differentiable,
-with the
-soft implementation and the original DTW algorithm, both
-being the same when 
-S2. Additional Results
-We include in this section a more extended set of results.
-First, we include results for the scenes room (see Figures 17
-to 20), chess (see Figures 21 to 24), and robots (see Fig-
-ures 25 to 28) from the Sitzmann et al. dataset [43]. Then,
-we include results for the five scenes from the Rai et al.
-dataset [39] used in comparisons throughout the main pa-
-per: train (see Figures 29 to 32), resort (see Figures 33
-to 36), square (see Figures 37 to 40), snow (see Figures 41
-to 44), and museum (see Figures 45 to 48).
-S3. Ground Truth Scanpaths for Comparison
-Scenes
-We include in Figures 49 to 53 sets of ground truth scan-
-paths for all the images shown in Figure 4 in the main pa-
-per, which is devoted to comparisons of our method against
-other models; and in Figures 54 to 56 sets of ground truth
-scanpaths for the three images from our test set from Sitz-
-mann et al.’s dataset.
-S4. Additional Details on our Training Process
-In addition to the details commented in Section 3.5 in
-the main paper, our generator trains two cycles per discrim-
-inator cycle, to avoid the latter from surpassing the former.
-To enhance the training process, we also resort to a mini-
-batching strategy: Instead of inputting to our model a setcontaining all available scanpaths for a given image, we
-split our data in different mini-batches of eight scanpaths
-each. This way, the same image is input in our network mul-
-tiple times per epoch, also allowing more images to be in-
-cluded in the same batch, and therefore enhancing the train-
-ing process. We trained our model for 217 epochs, as we
-found that epoch to yield the better evaluation results.
-S5. Additional Details on Metrics and Evalua-
-tion
-Throughout this work, we evaluate our model and com-
-pare to state-of-the-art works by means of several widely
-used metrics, recently reviewed by Fahimi and Bruce [17].
-Table 3 shows a list of these metrics, indicating which ones
-take into account position and/or order of gaze points. In
-the following, we briefly introduce these metrics (please re-
-fer to Fahimi and Bruce [17] for a formal description):
-• Levenshtein distance: Transforms scanpaths into
-strings, and then calculates the minimum number of
-single-character edits (insertions, deletions, or substi-
-tutions) required to change one string (scanpath) into
-the other. All edits costs are treated equally.
-• ScanMatch: Improved version of Levenshtein dis-
-tance. Different from Levenshtein distance, Scan-
-Match takes into account semantic information (as a
-score matrix), and can even take into account duration
-of data points. This way, each of the edit operations
-can be differently weighted.
-• Hausdorff distance: Represents the degree of mis-
-match between two sets by measuring the farthest spa-
-tial distance from one set to the other, i.e., the distance
-between two different curves.
-• Frechet distance: Similar to Hausdorff distance, it
-measures the similarity between curves. However,
-Frechet disatance takes into account both the position
-and ordering of all the points in the curves.
-• Dynamic time warping: Metric that compares two
-time-series with varying (and differing) lengths to
-find an optimal path to match both sequences while
-preserving boundary, continuity, and monotonicity to
-make sure that the path respects time.
-• Time delay embedding: Splits a scanpath into sev-
-eral sub-samples, i.e., small sub-scanpaths. This met-
-rics calculates a similarity score by performing several
-pair-wise Hausdorff comparisons over sub-samples
-from both scanpaths to compare.
-• Recurrence: Measures the percentage of gaze points
-that match (are close) between the two scanpaths.• Determinism: Percentage of cross-recurrent points that
-form diagonal lines (i.e., percentage of gaze trajecto-
-ries common to both scanpaths).
-• Laminarity: Measures locations that were fixated in
-detail in one of the scanpaths, but only fixated briefly
-in the other scanpath. This way, it indicates whether
-specific areas of a scene are repeatedly fixated.
-• Center of recurrence mass: Defined as the distance of
-the center of gravity from the main diagonal, indicates
-the dominant lag of cross recurrences, i.e., whether the
-same gaze point in both scanpaths tends to occur close
-in time.
-Table 3. Set of metrics to quantitatively evaluate scanpath similar-
-ity [17]. Each metric specializes in specific aspects of the scan-
-paths, and as a result using any of them in isolation may not be
-representative.
-Metric Abrv Position Order
-Levenshtein distance LEV 3 3
-ScanMatch SMT 3 3
-Hausdorff distance HAU 3 7
-Frechet distance FRE 3 3
-Dynamic time warping DTW 3 3
-Time delay embedding TDE 3 7
-Recurrence REC 3 7
-Determinism DET 7 3
-Laminarity LAM 7 7
-Center of recurrence mass COR 7 7
-Our model is stochastic by nature. This means that the
-scanpaths that it generates for a given scene are always dif-
-ferent, simulating observer variability. We have analyzed
-whether the reported metrics vary depending on the num-
-ber of scanpaths generated, to asses the stability and overall
-goodness of our model. Results can be seen in Table 4
-We include in Table 5 the evaluation results with the full
-set of metrics shown in Table 3 (extension to Table 1 in the
-main paper), and in Tables 6 and 7 the evaluation results of
-our ablation studies over the full set of metrics (extension to
-Table 2 in the main paper).
-Images for one of our test sets belong to Rai et al.’s
-dataset [39]. This dataset is larger than Sitzmann et al.’s
-in size (number of images), but provides gaze data in the
-form of fixations with associated timestamps, and not the
-raw gaze points. Note that most of the metrics proposed in
-the literature for scanpath similarity are designed to work
-with time series of different length, and do not necessarily
-assume a direct pairwise equivalence, making them valid to
-compare our generated scanpaths to the ground truth ones
-from Rai et al.’s dataset.Table 4. Quantitative results of our model with sets of generated
-scanpaths with different number of samples. Our results are stable
-regardless the number of generated samples.
-Dataset # of samples LEV#DTW#REC"DET"
-Test set from
-Sitzmann et al.100 46.19 1925.20 4.50 2.33
-800 46.10 1916.26 4.75 2.34
-2500 46.15 1921.95 4.82 2.32
-Human BL 43.11 1843.72 7.81 4.07
-Rai et al.’s
-dataset100 40.95 1548.86 1.91 1.85
-800 40.94 1542.82 1.86 1.86
-2500 40.99 1549.59 1.72 1.87
-Human BL 39.59 1495.55 2.33 2.31
-S6. Behavioral Evaluation
-In this section, we include further analysis and additional
-details on behavioral aspects of our scanpaths, extending
-Section 4.5 in the main paper.
-Temporal and spatial coherence As discussed in the
-main paper, our generated scanpaths have a degree of
-stochasticity, and different patterns arise depending on
-users’ previous history. To assess whether our scanpaths
-actually follow a coherent pattern, we generate a set of ran-
-dom scanpaths for each of the scenes in our test dataset, and
-separate them according to the longitudinal region where
-the scanpath begins ( e.g.,[0;40);[40;80), etc.). Then,
-we estimate the probability density of the generated scan-
-paths from each starting region using kernel density esti-
-mation (KDE) for each timestamp. We include the com-
-plete KDE results for the three images from our test set in
-Figures 11 to 16, for different starting regions, at different
-timestamps, and computed over 1000 generated scanpaths.
-During the first seconds (first column), gaze points tend to
-stay in a smaller area, and closer to the starting region; as
-time progresses, they exhibit a more exploratory behavior
-with higher divergence, and eventually may reach a conver-
-gence close to regions of interest. We can also see how the
-behavior can differ depending on the starting region.
-Exploration time As introduced in the main paper, we
-also explore the time that users took to move their eyes to a
-certain longitude relative to their starting point, and measure
-how long it takes for users to fully explore the scene. We in-
-clude in Figure 8 the comparison between ground truth and
-generated scanpaths in terms of time to explore the scene,
-for all the three scenes from our test set ( room ,chess , and
-robots ), both individual and aggregated. We can see how
-the speed and exploration time are very similar between real
-and generated data.
-S7. Applications of the Model
-Our model is able to generate plausible 30-second scan-
-paths, drawn from a distribution that mimics the behavior ofhuman observers. As we briefly discuss through the paper,
-this enables a number of applications, starting with avoiding
-the need to recruit and measure gaze from high numbers of
-observers in certain scenarios. We show here two applica-
-tions of our model, virtual scene design and scanpath-driven
-video thumbnail creation for static 360images, and discuss
-other potential application scenarios.
-Virtual scene design In an immersive environment, the
-user has control over the camera when exploring it. This
-poses a challenge to content creators and designers, who
-have to learn from experience how to layout the scene to
-elicit a specific viewing or exploration behavior. This is
-not only a problem in VR, but has also received attention
-in,e.g., manga composition [9] or web design [38]. How-
-ever, actually measuring gaze from a high enough number
-of users to determine optimal layouts can be challenging
-and time-consuming. While certain goals may require real
-users, others can make use of our model to generate plausi-
-ble and realistic generated observers.
-As a proof of concept, we have analyzed our model’s
-ability to adapt its behavior to different layouts of a scene
-(Figure 9). Specifically, we have removed certain elements
-from a scene, and run our model to analyze whether these
-changes affect the behavior of our generated scanpaths. We
-plot the resulting probability density (using KDE, see Sec-
-tion S6) as a function of time. The presence of different ele-
-ments in the scene affects the general viewing behavior, in-
-cluding viewing direction, or time spent on a certain region.
-These examples are particularly promising if we consider
-that our model is trained with a relatively small number of
-generic scenes.
-Scanpath-driven video thumbnails of static 360images
-360images capture the full sphere and are thus unintuitive
-when projected into a conventional 2D image. To address
-this problem, a number of approaches have proposed to re-
-target 360images or videos to 2D [46, 43, 45]. In the case
-of images, extracting a representative 2D visualization of
-the 360image can be helpful to provide a thumbnail of it,
-for example as a preview on a social media platform. How-
-ever, these thumbnails are static. The Ken Burns effect can
-be used to animate static images by panning and zooming
-a cropping window over a static image. In the context of
-360, however, it seems unclear what the trajectory of such
-a moving window would be.
-To address this question, we leverage our generated scan-
-paths to drive a Ken Burns–like video thumbnail of a static
-panorama. For this purpose, we use an average scanpath,
-computed as the probability density of several generated
-scanpaths using KDE (see Section S6), as the trajectory of
-the virtual camera. Specifically, KDE allows us to find the
-point of highest probability, along with its variance, of allTable 5. Quantitative comparison of our model against different approaches, following the metrics introduced in Table 1. We evaluate our
-model over the test set we separated from Sitzmann et al.’s dataset, and compare against Saltinet [2]. On the other hand, we validate our
-model over Rai et al.’s dataset, and compare us against Zhu et al.’s [62], whose results over this dataset were provided by the authors; and
-against SaltiNet, which was trained over that specific dataset (*). HB accounts for the human baseline, computed with the set of ground-
-truth scanpaths. We also compute a lower baseline, computed by randomly sampling the image. The arrow next to each metric indicates
-whether higher or lower is better. Best results are in bold.
-Dataset Method LEV#SMT"HAU#FRE# DTW#TDE#REC"DET"LAM"CORM"
-Test set from
-Sitzmann et al.Random BL 52.33 0.22 59.88 146.39 2370.56 27.93 0.47 0.93 9.19 33.19
-SaltiNet 48.00 0.18 64.23 149.34 1928.85 28.19 1.45 1.78 10.45 29.23
-ScanGAN360 (ours) 46.15 0.39 43.28 141.23 1921.95 18.62 4.82 2.32 24.51 35.78
-Human BL 43.11 0.43 41.38 142.91 1843.72 16.05 7.81 4.07 24.69 35.32
-Rai et al’s
-datasetRandom BL 43.11 0.17 65.71 144.73 1659.75 35.41 0.21 0.94 4.30 19.08
-SaltiNet()48.07 0.18 63.86 148.76 1928.41 28.42 1.43 1.81 10.22 29.33
-Zhu et al. 43.55 0.20 73.09 136.37 1744.20 30.62 1.64 1.50 9.18 26.05
-ScanGAN360 (ours) 40.99 0.24 61.86 139.10 1549.59 28.14 1.72 1.87 12.23 26.15
-Human BL 39.59 0.24 66.23 136.70 1495.55 27.24 2.33 2.31 14.36 23.14
-Table 6. Results of our ablation study over Sitzmann et al.’s test set. We take a basic GAN strategy as baseline, and evaluate the effects
-of adding a second term into our generator’s loss function. We ablate a model with an MSE error (as used in the only GAN approach for
-scanpath generation so far [3]), and compare it against our spherical DTW approach. We also analyze the importance of the CoordConv
-layer, whhose absence slightly worsen the results. See Section 4 in the main paper for further discussion. Qualitative results of this ablation
-study can be seen in Figure 5 in the main paper.
-Metric LEV#SMT"HAU#FRE# DTW#TDE#REC"DET"LAM"CORM"
-Basic GAN 49.42 0.36 43.69 145.95 2088.44 20.05 3.01 1.74 18.55 34.51
-MSE 48.90 0.37 42.27 133.24 1953.21 19.48 2.41 1.73 18.47 37.34
-DTWsph(no CoordConv) 47.82 0.37 46.59 144.92 1988.38 20.13 3.67 1.99 18.09 35.66
-DTWsph(ours) 46.15 0.39 43.28 141.23 1921.95 18.62 4.82 2.32 24.21 35.78
-Human BL 43.11 0.43 41.38 142.91 1843.72 16.05 7.81 4.07 24.69 35.32
-Table 7. Results of our ablation study over Rai et al.’s dataset. We take a basic GAN strategy as baseline, and evaluate the effects of adding
-a second term into our generator’s loss function. We ablate a model with an MSE error (as used in the only GAN approach for scanpath
-generation so far [3]), and compare it against our spherical DTW approach. We also analyze the importance of the CoordConv layer,
-whhose absence slightly worsen the results. See Section 4 in the main paper for further discussion. Qualitative results of this ablation study
-can be seen in Figure 5 in the main paper.
-Metric LEV#SMT"HAU#FRE# DTW#TDE#REC"DET"LAM"CORM"
-Basic GAN 41.73 0.23 59.11 142.42 1542.52 28.40 0.99 1.47 8.08 24.55
-MSE 41.81 0.23 61.30 139.59 1541.44 28.66 1.01 1.51 8.56 24.45
-DTWsph(no CoordConv) 41.42 0.23 61.55 148.13 1610.10 28.78 1.61 1.65 10.25 24.68
-DTWsph(ours) 40.99 0.24 61.86 139.10 1549.59 28.14 1.72 1.87 12.23 26.15
-Human BL 39.59 0.24 66.23 136.70 1495.55 27.24 2.33 2.31 14.36 23.14
-generated scanpaths at any point in time. Note that this
-point is not necessarily the average of the scanpaths. We
-use the time-varying center point as the center of our 2D
-viewport, and its variance to drive the FOV or zoom of the
-moving viewport.
-Figure 10 shows several representative steps of this pro-
-cess for two different scenes ( chess andstreet ). Full videos
-of several scenes are included in the supplementary video.
-The generated Ken Burns–style panorama previews look
-like a human observer exploring these panorama and pro-
-vide a very intuitive preview of the complex scenes they
-depict.Other applications Our model has the potential to en-
-able other applications beyond what we have shown in this
-section. One such example is gaze simulation for virtual
-avatars . When displaying or interacting with virtual char-
-acters, eye gaze is one of the most critical, yet most diffi-
-cult, aspects to simulate [40]. Accurately simulating gaze
-behavior not only aids in conveying realism, but can also
-provide additional information such as signalling interest,
-aiding the conversation through non-verbal cues, facilitating
-turn-taking in multi-party conversations, or indicating atten-
-tiveness, among others. Given an avatar immersed within
-a virtual scene, generating plausible scanpaths conditioned
-by a 360image of their environment could be an efficient,
-affordable way of driving the avatar’s gaze behavior in aFigure 8. Time to explore each of the scenes from the Sitzmann et al. test set, together with their ground truth counterpart.
-Figure 9. Our model can be used to aid the design of virtual scenes. We show two examples, each with two possible layouts (original,
-and removing some significant elements). We generate a large number of scanpaths (virtual observers) starting from the same region, and
-compute their corresponding probability density function as a function of time, using KDE (see Section S6). room scene: The presence of
-the dining table and lamps (top) retains the viewers’ attention longer, while in their absence they move faster towards the living room area,
-performing a more linear exploration. gallery scene: When the central picture is present (top), the viewers linger there before splitting to
-both sides of the scene. In its absence, observers move towards the left, then explore the scene linearly in that direction.
-realistic manner.
-Another potential application of our model is its use for
-gaze-contingent rendering . These approaches have been
-proposed to save rendering time and bandwidth in VR sys-
-tems or drive the user’s accommodation. Eye trackers are
-required for these applications, but they are often too slow,making computationally efficient approaches for predicting
-gaze trajectories or landing positions important [1]. Our
-method for generating scanpaths could not only help proto-
-type and evaluate such systems in simulation, without the
-need for a physical eye tracker and actual users, but also in
-optimizing their latency and performance during runtime.Figure 10. Scanpath-driven video thumbnails of 360images. We
-propose a technique to generate these videos that results in relevant
-and intuitive explorations of the 360scenes. Top row: Points of
-highest probability at each time instant, displayed as scanpaths.
-These are used as a guiding trajectory for the virtual camera. Mid-
-dle rows: Two viewports from the guiding trajectory, correspond-
-ing to the temporal window with lowest variance. Bottom row: 2D
-images retargeted from those viewports. Please refer to the text for
-details.
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-Figure 25. Generated scanpaths for the robots scene.Figure 26. Generated scanpaths for the robots scene.
-Figure 27. Generated scanpaths for the robots scene.Figure 28. Generated scanpaths for the robots scene.
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-Figure 49. Ground truth scanpaths for the train scene.Figure 50. Ground truth scanpaths for the resort scene.
-Figure 51. Ground truth scanpaths for the snow scene.Figure 52. Ground truth scanpaths for the museum scene.
-Figure 53. Ground truth scanpaths for the square scene.Figure 54. Ground truth scanpaths for the room scene.
-Figure 55. Ground truth scanpaths for the chess scene.Figure 56. Ground truth scanpaths for the robots scene.

txt/2103.14651.txt

@@ -1,1336 +0,0 @@
-Local Explanations via Necessity and Sufficiency:
-Unifying Theory and Practice
-David S. Watson*1Limor Gultchin*2,3Ankur Taly4Luciano Floridi5,3
-*Equal contribution1Department of Statistical Science, University College London, London, UK
-2Department of Computer Science, University of Oxford, Oxford, UK
-3The Alan Turing Institute, London, UK4Google Inc., Mountain View, USA
-5Oxford Internet Institute, University of Oxford, Oxford, UK
-Abstract
-Necessity and sufficiency are the building blocks
-of all successful explanations. Yet despite their im-
-portance, these notions have been conceptually un-
-derdeveloped and inconsistently applied in explain-
-able artificial intelligence (XAI), a fast-growing re-
-search area that is so far lacking in firm theoretical
-foundations. Building on work in logic, probabil-
-ity, and causality, we establish the central role of
-necessity and sufficiency in XAI, unifying seem-
-ingly disparate methods in a single formal frame-
-work. We provide a sound and complete algorithm
-for computing explanatory factors with respect to
-a given context, and demonstrate its flexibility and
-competitive performance against state of the art al-
-ternatives on various tasks.
-1 INTRODUCTION
-Machine learning algorithms are increasingly used in a va-
-riety of high-stakes domains, from credit scoring to medi-
-cal diagnosis. However, many such methods are opaque , in
-that humans cannot understand the reasoning behind partic-
-ular predictions. Post-hoc, model-agnostic local explanation
-tools (e.g., feature attributions, rule lists, and counterfactu-
-als) are at the forefront of a fast-growing area of research
-variously referred to as interpretable machine learning or
-explainable artificial intelligence (XAI).
-Many authors have pointed out the inconsistencies between
-popular XAI tools, raising questions as to which method
-is more reliable in particular cases [Mothilal et al., 2020a;
-Ramon et al., 2020; Fernández-Loría et al., 2020]. Theoret-
-ical foundations have proven elusive in this area, perhaps
-due to the perceived subjectivity inherent to notions such
-as “intelligible” and “relevant” [Watson and Floridi, 2020].
-Practitioners often seek refuge in the axiomatic guarantees
-of Shapley values, which have become the de facto stan-
-Figure 1: We describe minimal sufficient factors (here, sets
-of features) for a given input (top row), with the aim of
-preserving or flipping the original prediction. We report a
-sufficiency score for each set and a cumulative necessity
-score for all sets, indicating the proportion of paths towards
-the outcome that are covered by the explanation. Feature
-colors indicate source of feature values (input or reference).
-dard in many XAI applications, due in no small part to their
-attractive theoretical properties [Bhatt et al., 2020]. How-
-ever, ambiguities regarding the underlying assumptions of
-the method [Kumar et al., 2020] and the recent prolifera-
-tion of mutually incompatible implementations [Sundarara-
-jan and Najmi, 2019; Merrick and Taly, 2020] have com-
-plicated this picture. Despite the abundance of alternative
-XAI tools [Molnar, 2021], a dearth of theory persists. This
-has led some to conclude that the goals of XAI are under-
-specified [Lipton, 2018], and even that post-hoc methods do
-more harm than good [Rudin, 2019].
-We argue that this lacuna at the heart of XAI should be filled
-by a return to fundamentals – specifically, to necessity and
-sufficiency . As the building blocks of all successful expla-
-nations, these dual concepts deserve a privileged position
-in the theory and practice of XAI. Following a review of re-
-lated work (Sect. 2), we operationalize this insight with a
-unified framework (Sect. 3) that reveals unexpected affinities
-Accepted for the 37thConference on Uncertainty in Artificial Intelligence (UAI 2021).arXiv:2103.14651v2  [cs.LG]  10 Jun 2021between various XAI tools and probabilities of causation
-(Sect. 4). We proceed to implement a novel procedure for
-computing model explanations that improves upon the state
-of the art in various quantitative and qualitative comparisons
-(Sect. 5). Following a brief discussion (Sect. 6), we conclude
-with a summary and directions for future work (Sect. 7).
-We make three main contributions. (1) We present a formal
-framework for XAI that unifies several popular approaches,
-including feature attributions, rule lists, and counterfactu-
-als. (2) We introduce novel measures of necessity and suf-
-ficiency that can be computed for any feature subset. The
-method enables users to incorporate domain knowledge,
-search various subspaces, and select a utility-maximizing
-explanation. (3) We present a sound and complete algorithm
-for identifying explanatory factors, and illustrate its perfor-
-mance on a range of tasks.
-2 NECESSITY AND SUFFICIENCY
-Necessity and sufficiency have a long philosophical tradi-
-tion [Mackie, 1965; Lewis, 1973; Halpern and Pearl, 2005b],
-spanning logical, probabilistic, and causal variants. In propo-
-sitional logic, we say that xis a sufficient condition for y
-iffx!y, andxis a necessary condition for yiffy!x.
-So stated, necessity and sufficiency are logically converse .
-However, by the law of contraposition, both definitions ad-
-mit alternative formulations, whereby sufficiency may be
-rewritten as:y!:xand necessity as:x!:y. By pair-
-ing the original definition of sufficiency with the latter def-
-inition of necessity (and vice versa), we find that the two
-concepts are also logically inverse .
-These formulae suggest probabilistic relaxations, measur-
-ingx’s sufficiency for ybyP(yjx)andx’s necessity for y
-byP(xjy). Because there is no probabilistic law of contra-
-position, these quantities are generally uninformative w.r.t.
-P(:xj:y)andP(:yj:x), which may be of independent
-interest. Thus, while necessity is both the converse and in-
-verse of sufficiency in propositional logic, the two formula-
-tions come apart in probability calculus. We revisit the dis-
-tinction between probabilistic conversion and inversion in
-Rmk. 1 and Sect. 4.
-These definitions struggle to track our intuitions when we
-consider causal explanations [Pearl, 2000; Tian and Pearl,
-2000]. It may make sense to say in logic that if xis a neces-
-sary condition for y, thenyis a sufficient condition for x;
-it does not follow that if xis a necessary cause ofy, theny
-is a sufficient cause ofx. We may amend both concepts us-
-ingcounterfactual probabilities – e.g., the probability that
-Alice would still have a headache if she had not taken an as-
-pirin, given that she does not have a headache and did take
-an aspirin. Let P(yxjx0;y0)denote such a quantity, to be
-read as “the probability that Ywould equal yunder an in-
-tervention that sets Xtox, given that we observe X=x0andY=y0.” Then, according to Pearl [2000, Ch. 9], the
-probability that xis a sufficient cause of yis given by
-suf(x;y) :=P(yxjx0;y0), and the probability that xis a
-necessary cause of yis given by nec(x;y) :=P(y0
-x0jx;y):
-Analysis becomes more difficult in higher dimensions,
-where variables may interact to block or unblock causal path-
-ways. VanderWeele and Robins [2008] analyze sufficient
-causal interactions in the potential outcomes framework,
-refining notions of synergism without monotonicity con-
-straints. In a subsequent paper, VanderWeele and Richard-
-son [2012] study the irreducibility and singularity of interac-
-tions in sufficient-component cause models. Halpern [2016]
-devotes an entire monograph to the subject, providing vari-
-ous criteria to distinguish between subtly different notions
-of “actual causality”, as well as “but-for” (similar to nec-
-essary) and sufficient causes. These authors generally limit
-their analyses to Boolean systems with convenient structural
-properties, e.g. conditional ignorability and the stable unit
-treatment value assumption [Imbens and Rubin, 2015]. Op-
-erationalizing their theories in a practical method without
-such restrictions is one of our primary contributions.
-Necessity and sufficiency have begun to receive explicit at-
-tention in the XAI literature. Ribeiro et al. [2018a] propose
-a bandit procedure for identifying a minimal set of Boolean
-conditions that entails a predictive outcome (more on this in
-Sect. 4). Dhurandhar et al. [2018] propose an autoencoder
-for learning pertinent negatives and positives, i.e. features
-whose presence or absence is decisive for a given label,
-while Zhang et al. [2018] develop a technique for generat-
-ing symbolic corrections to alter model outputs. Both meth-
-ods are optimized for neural networks, unlike the model-
-agnostic approach we develop here.
-Another strand of research in this area is rooted in logic pro-
-gramming. Several authors have sought to reframe XAI as
-either a SAT [Ignatiev et al., 2019; Narodytska et al., 2019]
-or a set cover problem [Lakkaraju et al., 2019; Grover et al.,
-2019], typically deriving approximate solutions on a pre-
-specified subspace to ensure computability in polynomial
-time. We adopt a different strategy that prioritizes complete-
-ness over efficiency, an approach we show to be feasible in
-moderate dimensions (see Sect. 6 for a discussion).
-Mothilal et al. [2020a] build on Halpern [2016]’s definitions
-of necessity and sufficiency to critique popular XAI tools,
-proposing a new feature attribution measure with some pur-
-ported advantages. Their method relies on the strong as-
-sumption that predictors are mutually independent. Galho-
-tra et al. [2021] adapt Pearl [2000]’s probabilities of cau-
-sation for XAI under a more inclusive range of data gen-
-erating processes. They derive analytic bounds on multidi-
-mensional extensions of nec andsuf, as well as an algo-
-rithm for point identification when graphical structure per-
-mits. Oddly, they claim that non-causal applications of ne-
-cessity and sufficiency are somehow “incorrect and mislead-ing” (p. 2), a normative judgment that is inconsistent with
-many common uses of these concepts.
-Rather than insisting on any particular interpretation of ne-
-cessity and sufficiency, we propose a general framework that
-admits logical, probabilistic, and causal interpretations as
-special cases. Whereas previous works evaluate individual
-predictors, we focus on feature subsets , allowing us to detect
-and quantify interaction effects. Our formal results clarify
-the relationship between existing XAI methods and proba-
-bilities of causation, while our empirical results demonstrate
-their applicability to a wide array of tasks and datasets.
-3 A UNIFYING FRAMEWORK
-We propose a unifying framework that highlights the role of
-necessity and sufficiency in XAI. Its constituent elements
-are described below.
-Target function. Post-hoc explainability methods assume
-access to a target function f:X7!Y , i.e. the model whose
-prediction(s) we seek to explain. For simplicity, we restrict
-attention to the binary setting, with Y2f0;1g. Multi-class
-extensions are straightforward, while continuous outcomes
-may be accommodated via discretization. Though this in-
-evitably involves some information loss, we follow authors
-in the contrastivist tradition in arguing that, even for con-
-tinuous outcomes, explanations always involve a juxtapo-
-sition (perhaps implicit) of “fact and foil” [Lipton, 1990].
-For instance, a loan applicant is probably less interested in
-knowing why her credit score is precisely ythan she is in
-discovering why it is below some threshold (say, 700). Of
-course, binary outcomes can approximate continuous values
-with arbitrary precision over repeated trials.
-Context. The contextDis a probability distribution over
-which we quantify sufficiency and necessity. Contexts may
-be constructed in various ways but always consist of at least
-some input (point or space) and reference (point or space).
-For instance, we may want to compare xiwith all other
-samples, or else just those perturbed along one or two axes,
-perhaps based on some conditioning event(s).
-In addition to predictors and outcomes, we optionally in-
-clude information exogenous to f. For instance, if any
-events were conditioned upon to generate a given refer-
-ence sample, this information may be recorded among a
-set of auxiliary variables W. Other examples of potential
-auxiliaries include metadata or engineered features such as
-those learned via neural embeddings. This augmentation al-
-lows us to evaluate the necessity and sufficiency of factors
-beyond those found in X. Contextual data take the form
-Z= (X;W)D . The distribution may or may not en-
-code dependencies between (elements of) Xand (elements
-of)W. We extend the target function to augmented inputs
-by definingf(z) :=f(x).Factors. Factors pick out the properties whose necessity
-and sufficiency we wish to quantify. Formally, a factor
-c:Z 7!f 0;1gindicates whether its argument satisfies
-some criteria with respect to predictors or auxiliaries. For
-instance, if xis an input to a credit lending model, and w
-contains information about the subspace from which data
-were sampled, then a factor could be c(z) =1[x[gender =
-“female” ]^w[do(income>$50k)]], i.e. checking if zis
-female and drawn from a context in which an intervention
-fixes income at greater than $50k. We use the term “factor”
-as opposed to “condition” or “cause” to suggest an inclusive
-set of criteria that may apply to predictors xand/or auxil-
-iaries w. Such criteria are always observational w.r.t. zbut
-may be interventional or counterfactual w.r.t. x. We assume
-a finite space of factors C.
-Partial order. When multiple factors pass a given neces-
-sity or sufficiency threshold, users will tend to prefer some
-over others. For instance, factors with fewer conditions are
-often preferable to those with more, all else being equal;
-factors that change a variable by one unit as opposed to two
-are preferable, and so on. Rather than formalize this pref-
-erence in terms of a distance metric, which unnecessarily
-constrains the solution space, we treat the partial ordering
-as primitive and require only that it be complete and transi-
-tive. This covers not just distance-based measures but also
-more idiosyncratic orderings that are unique to individual
-agents. Ordinal preferences may be represented by cardi-
-nal utility functions under reasonable assumptions (see, e.g.,
-[von Neumann and Morgenstern, 1944]).
-We are now ready to formally specify our framework.
-Definition 1 (Basis) .Abasis for computing necessary and
-sufficient factors for model predictions is a tuple B=
-hf;D;C;i, wherefis a target function, Dis a context,C
-is a set of factors, and is a partial ordering on C.
-3.1 EXPLANATORY MEASURES
-For some fixed basis B=hf;D;C;i, we define the fol-
-lowing measures of sufficiency and necessity, with probabil-
-ity taken overD.
-Definition 2 (Probability of Sufficiency) .The probability
-thatcis a sufficient factor for outcome yis given by:
-PS(c;y) :=P(f(z) =yjc(z) = 1):
-The probability that factor set C=fc1;:::;ckgis sufficient
-foryis given by:
-PS(C;y) :=P(f(z) =yjkX
-i=1ci(z)1):
-Definition 3 (Probability of Necessity) .The probability
-thatcis a necessary factor for outcome yis given by:
-PN(c;y) :=P(c(z) = 1jf(z) =y):The probability that factor set C=fc1;:::;ckgis neces-
-sary foryis given by:
-PN(C;y) :=P(kX
-i=1ci(z)1jf(z) =y):
-Remark 1. These probabilities can be likened to the “pre-
-cision” (positive predictive value) and “recall” (true posi-
-tive rate) of a (hypothetical) classifier that predicts whether
-f(z) =ybased on whether c(z) = 1 . By examining the
-confusion matrix of this classifier, one can define other
-related quantities, e.g. the true negative rate P(c(z) =
-0jf(z)6=y)and the negative predictive value P(f(z)6=
-yjc(z) = 0) , which are contrapositive transformations of
-our proposed measures. We can recover these values exactly
-viaPS(1c;1y)andPN(1c;1y), respectively.
-When necessity and sufficiency are defined as probabilistic
-inversions (rather than conversions), such transformations
-are impossible.
-3.2 MINIMAL SUFFICIENT FACTORS
-We introduce Local Explanations via Necessity and Suffi-
-ciency (LENS), a procedure for computing explanatory fac-
-tors with respect to a given basis Band threshold parame-
-ter(see Alg. 1). First, we calculate a factor’s probability
-of sufficiency (see probSuff ) by drawing nsamples from
-Dand taking the maximum likelihood estimate ^PS(c;y).
-Next, we sort the space of factors w.r.t. in search of those
-that are-minimal.
-Definition 4 (-minimality) .We say thatcis-minimal iff
-(i)PS(c;y)and (ii) there exists no factor c0such that
-PS(c0;y)andc0c.
-Since a factor is necessary to the extent that it covers all
-possible pathways towards a given outcome, our next step is
-to span the-minimal factors and compute their cumulative
-PN (seeprobNec ). As a minimal factor cstands for all c0
-such thatcc0, in reporting probability of necessity, we
-expandCto its upward closure.
-Thms. 1 and 2 state that this procedure is optimal in a sense
-that depends on whether we assume access to oracle or
-sample estimates of PS(see Appendix A for all proofs).
-Theorem 1. With oracle estimates PS(c;y)for allc2C,
-Alg. 1 is sound and complete. That is, for any Creturned
-by Alg. 1 and all c2C,cis-minimal iff c2C.
-Population proportions may be obtained if data fully saturate
-the spaceD, a plausible prospect for categorical variables
-of low to moderate dimensionality. Otherwise, proportions
-will need to be estimated.
-Theorem 2. With sample estimates ^PS(c;y)for allc2C,
-Alg. 1 is uniformly most powerful. That is, Alg. 1 identifiesthe most-minimal factors of any method with fixed type I
-error.
-Multiple testing adjustments can easily be accommodated,
-in which case modified optimality criteria apply [Storey,
-2007].
-Remark 2. We take it that the main quantity of interest
-in most applications is sufficiency, be it for the original or
-alternative outcome, and therefore define -minimality w.r.t.
-sufficient (rather than necessary) factors. However, necessity
-serves an important role in tuning , as there is an inherent
-trade-off between the parameters. More factors are excluded
-at higher values of , thereby inducing lower cumulative
-PN; more factors are included at lower values of , thereby
-inducing higher cumulative PN. See Appendix B.
-Algorithm 1 LENS
-1:Input:B=hf;D;C;i;
-2:Output: Factor setC,(8c2C)PS(c;y);PN (C;y)
-3:Sample ^D=fzign
-i=1D
-4:function probSuff (c,y)
-5: n(c&y) =Pn
-i=11[c(zi) = 1^f(zi) =y]
-6: n(c) =Pn
-i=1c(zi)
-7: return n(c&y) / n(c)
-8:function probNec (C,y, upward_closure_flag)
-9: ifupward_closure_flag then
-10:C=fcjc2C^9c02C:c0cg
-11: end if
-12: n(C&y) =Pn
-i=11[Pk
-j=1cj(zi)1^f(zi) =y]
-13: n(y) =Pn
-i=11[f(zi) =y]
-14: return n(C&y) / n(y)
-15:function minimalSuffFactors (y,, sample_flag, )
-16: sorted_factors = topological _sort(C;)
-17: cands = []
-18: forcin sorted_factors do
-19: if9(c0;_)2cands :c0cthen
-20: continue
-21: end if
-22: ps =probSuff (c,y)
-23: ifsample_flag then
-24: p =binom.test (n(c&y), n(c), , alt =>)
-25: ifpthen
-26: cands.append( c, ps)
-27: end if
-28: else if psthen
-29: cands.append( c, ps)
-30: end if
-31: end for
-32: cum_pn = probNec (fcj(c;_)2candsg;y, TRUE)
-33: return cands, cum_pn4 ENCODING EXISTING MEASURES
-Explanatory measures can be shown to play a central role in
-many seemingly unrelated XAI tools, albeit under different
-assumptions about the basis tuple B. In this section, we
-relate our framework to a number of existing methods.
-Feature attributions. Several popular feature attribution
-algorithms are based on Shapley values [Shapley, 1953],
-which decompose the predictions of any target function as a
-sum of weights over dinput features:
-f(xi) =0+dX
-j=1j; (1)
-where0represents a baseline expectation and jthe
-weight assigned to Xjat point xi. Letv: 2d7!Rbe a
-value function such that v(S)is the payoff associated with
-feature subset S[d]andv(f;g) = 0 . Define the comple-
-mentR= [d]nSsuch that we may rewrite any xias a pair
-of subvectors, (xS
-i;xR
-i). Payoffs are given by:
-v(S) =E[f(xS
-i;XR)]; (2)
-although this introduces some ambiguity regarding the ref-
-erence distribution for XR(more on this below). The Shap-
-ley valuejis thenj’s average marginal contribution to all
-subsets that exclude it:
-j=X
-S[d]nfjgjSj!(djSj1)!
-d!v(S[fjg)v(S):(3)
-It can be shown that this is the unique solution to the attri-
-bution problem that satisfies certain desirable properties, in-
-cluding efficiency, linearity, sensitivity, and symmetry.
-Reformulating this in our framework, we find that the value
-functionvis a sufficiency measure. To see this, let each
-zD be a sample in which a random subset of variables
-Sare held at their original values, while remaining features
-Rare drawn from a fixed distribution D(
jS).1
-Proposition 1. LetcS(z) = 1 iffxzwas constructed
-by holding xSfixed and sampling XRaccording toD(
jS).
-Thenv(S) =PS(cS;y).
-Thus, the Shapley value jmeasuresXj’s average marginal
-increase to the sufficiency of a random feature subset. The
-advantage of our method is that, by focusing on particular
-subsets instead of weighting them all equally, we disregard
-irrelevant permutations and home in on just those that meet
-a-minimality criterion. Kumar et al. [2020] observe that,
-1The diversity of Shapley value algorithms is largely due to
-variation in how this distribution is defined. Popular choices in-
-clude the marginal P(XR)[Lundberg and Lee, 2017]; conditional
-P(XRjxS)[Aas et al., 2019]; and interventional P(XRjdo(xS))
-[Heskes et al., 2020] distributions.“since there is no standard procedure for converting Shapley
-values into a statement about a model’s behavior, developers
-rely on their own mental model of what the values represent”
-(p. 8). By contrast, necessary and sufficient factors are more
-transparent and informative, offering a direct path to what
-Shapley values indirectly summarize.
-Rule lists. Rule lists are sequences of if-then statements
-that describe a hyperrectangle in feature space, creating par-
-titions that can be visualized as decision or regression trees.
-Rule lists have long been popular in XAI. While early work
-in this area tended to focus on global methods [Friedman
-and Popescu, 2008; Letham et al., 2015], more recent efforts
-have prioritized local explanation tasks [Lakkaraju et al.,
-2019; Sokol and Flach, 2020].
-We focus in particular on the Anchors algorithm [Ribeiro
-et al., 2018a], which learns a set of Boolean conditions A
-(the eponymous “anchors”) such that A(xi) = 1 and
-PD(xjA)(f(xi) =f(x)): (4)
-The lhs of Eq. 4 is termed the precision , prec(A), and proba-
-bility is taken over a synthetic distribution in which the con-
-ditions inAhold while other features are perturbed. Once 
-is fixed, the goal is to maximize coverage , formally defined
-asE[A(x) = 1] , i.e. the proportion of datapoints to which
-the anchor applies.
-The formal similarities between Eq. 4 and Def. 2 are imme-
-diately apparent, and the authors themselves acknowledge
-that Anchors are intended to provide “sufficient conditions”
-for model predictions.
-Proposition 2. LetcA(z) = 1 iffA(x) = 1 . Then
-prec(A) =PS(cA;y).
-While Anchors outputs just a single explanation, our method
-generates a ranked list of candidates, thereby offering a
-more comprehensive view of model behavior. Moreover, our
-necessity measure adds a mode of explanatory information
-entirely lacking in Anchors.
-Counterfactuals. Counterfactual explanations identify
-one or several nearest neighbors with different outcomes, e.g.
-all datapoints xwithin an-ball of xisuch that labels f(x)
-andf(xi)differ (for classification) or f(x)> f(xi) +
-(for regression).2The optimization problem is:
-x= argmin
-x2CF(xi)cost(xi;x); (5)
-where CF(xi)denotes a counterfactual space such that
-f(xi)6=f(x)andcost is a user-supplied cost function, typ-
-ically equated with some distance measure. [Wachter et al.,
-2Confusingly, the term “counterfactual” in XAI refers to any
-point with an alternative outcome, which is distinct from the causal
-sense of the term (see Sect. 2). We use the word in both senses
-here, but strive to make our intended meaning explicit in each case.2018] recommend using generative adversarial networks
-to solve Eq. 5, while others have proposed alternatives de-
-signed to ensure that counterfactuals are coherent and ac-
-tionable [Ustun et al., 2019; Karimi et al., 2020a; Wexler
-et al., 2020]. As with Shapley values, the variation in these
-proposals is reducible to the choice of context D.
-For counterfactuals, we rewrite the objective as a search for
-minimal perturbations sufficient to flip an outcome.
-Proposition 3. Letcost be a function representing , and
-letcbe some factor spanning reference values. Then the
-counterfactual recourse objective is:
-c= argmin
-c2Ccost(c)s.t.PS(c;1y); (6)
-wheredenotes a decision threshold. Counterfactual out-
-puts will then be any zD such thatc(z) = 1 .
-Probabilities of causation. Our framework can describe
-Pearl [2000]’s aforementioned probabilities of causation,
-however in this case Dmust be constructed with care.
-Proposition 4. Consider the bivariate Boolean setting, as
-in Sect. 2. We have two counterfactual distributions: an in-
-put spaceI, in which we observe x;ybut intervene to set
-X=x0; and a reference space R, in which we observe x0;y0
-but intervene to set X=x. LetDdenote a uniform mixture
-over both spaces, and let auxiliary variable Wtag each sam-
-ple with a label indicating whether it comes from the origi-
-nal (W= 1) or contrastive ( W= 0) counterfactual space.
-Definec(z) =w. Then we have suf(x;y) =PS(c;y)and
-nec(x;y) =PS(1c;y0).
-In other words, we regard Pearl’s notion of necessity as suf-
-ficiency of the negated factor for the alternative outcome .
-By contrast, Pearl [2000] has no analogue for our proba-
-bility of necessity. This is true of any measure that defines
-sufficiency and necessity via inverse, rather than converse
-probabilities. While conditioning on the same variable(s)
-for both measures may have some intuitive appeal, it comes
-at a cost to expressive power. Whereas our framework can
-recover all four explanatory measures, corresponding to the
-classical definitions and their contrapositive forms, defini-
-tions that merely negate instead of transpose the antecedent
-and consequent are limited to just two.
-Remark 3. We have assumed that factors and outcomes
-are Boolean throughout. Our results can be extended to
-continuous versions of either or both variables, so long as
-c(Z)
-j=YjZ. This conditional independence holds when-
-everW
-j=YjX, which is true by construction since
-f(z) :=f(x). However, we defend the Boolean assump-
-tion on the grounds that it is well motivated by contrastivist
-epistemologies [Kahneman and Miller, 1986; Lipton, 1990;
-Blaauw, 2013] and not especially restrictive, given that parti-
-tions of arbitrary complexity may be defined over ZandY.
-Figure 2: Comparison of top kfeatures ranked by SHAP
-against the best performing LENS subset of size kin
-terms ofPS(c;y).German results are over 50 inputs;
-SpamAssassins results are over 25 inputs.
-5 EXPERIMENTS
-In this section, we demonstrate the use of LENS on a va-
-riety of tasks and compare results with popular XAI tools,
-using the basis configurations detailed in Table 1. A com-
-prehensive discussion of experimental design, including
-datasets and pre-processing pipelines, is left to Appendix
-C. Code for reproducing all results is available at https:
-//github.com/limorigu/LENS .
-Contexts. We consider a range of contexts Din our exper-
-iments. For the input-to-reference (I2R) setting, we replace
-input values with reference values for feature subsets S; for
-the reference-to-input (R2I) setting, we replace reference
-values with input values. We use R2I for examining suffi-
-ciency/necessity of the original model prediction, and I2R
-for examining sufficiency/necessity of a contrastive model
-prediction. We sample from the empirical data in all exper-
-iments, except in Sect. 5.3, where we assume access to a
-structural causal model (SCM).
-Partial Orderings. We consider two types of partial or-
-derings in our experiments. The first, subset , evaluates
-subset relationships. For instance, if c(z) =1[x[gender =
-“female” ]]andc0(z) = 1[x[gender =“female”^
-age40]], then we say that csubsetc0. The second,
-ccostc0:=csubsetc0^cost(c)cost(c0), adds the
-additional constraint that chas cost no greater than c0. The
-cost function could be arbitrary. Here, we consider distance
-measures over either the entire state space or just the inter-
-vention targets corresponding to c.
-5.1 FEATURE ATTRIBUTIONS
-Feature attributions are often used to identify the top- kmost
-important features for a given model outcome [Barocas et al.,
-2020]. However, we argue that these feature sets may not
-be explanatory with respect to a given prediction. To show
-this, we compute R2I and I2R sufficiency – i.e., PS(c;y)
-andPS(1c;1y), respectively – for the top- kmost in-
-fluential features ( k2[1;9]) as identified by SHAP [Lund-
-berg and Lee, 2017] and LENS. Fig. 2 shows results from
-the R2I setting for German credit [Dua and Graff, 2017]
-andSpamAssassin datasets [SpamAssassin, 2006]. OurTable 1: Overview of experimental settings by basis configuration.
-Experiment Datasets f DC 
-Attribution comparison German ,SpamAssassins Extra-Trees R2I, I2R Intervention targets -
-Anchors comparison: Brittle predictions IMDB LSTM R2I, I2R Intervention targets subset
-Anchors comparison: PS and Prec German Extra-Trees R2I Intervention targets subset
-Counterfactuals: Adverserial SpamAssassins MLP R2I Intervention targets subset
-Counterfactuals: Recourse, DiCE comparison Adult MLP I2R Full interventions cost
-Counterfactuals: Recourse, causal vs. non-causal German Extra-Trees I2Rcausal Full interventions cost
-method attains higher PSfor all cardinalities. We repeat
-the experiment over 50 inputs, plotting means and 95% con-
-fidence intervals for all k. Results indicate that our rank-
-ing procedure delivers more informative explanations than
-SHAP at any fixed degree of sparsity. Results from the I2R
-setting are in Appendix C.
-5.2 RULE LISTS
-Sentiment sensitivity analysis. Next, we use LENS to
-study model weaknesses by considering minimal factors
-with high R2I and I2R sufficiency in text models. Our
-goal is to answer questions of the form, “What are words
-with/without which our model would output the origi-
-nal/opposite prediction for an input sentence?” For this ex-
-periment, we train an LSTM network on the IMDB dataset
-for sentiment analysis [Maas et al., 2011]. If the model mis-
-labels a sample, we investigate further; if it does not, we
-inspect the most explanatory factors to learn more about
-model behavior. For the purpose of this example, we only
-inspect sentences of length 10 or shorter. We provide two
-examples below and compare with Anchors (see Table 2).
-Consider our first example: READ BOOK FORGET MOVIE is
-a sentence we would expect to receive a negative prediction,
-but our model classifies it as positive. Since we are inves-
-tigating a positive prediction, our reference space is condi-
-tioned on a negative label. For this model, the classic UNK
-token receives a positive prediction. Thus we opt for an al-
-ternative, PLATE . Performing interventions on all possible
-combinations of words with our token, we find the conjunc-
-tion of READ ,FORGET , and MOVIE is a sufficient factor for
-a positive prediction (R2I). We also find that changing any
-ofREAD ,FORGET , or MOVIE to PLATE would result in a
-negative prediction (I2R). Anchors, on the other hand, per-
-turbs the data stochastically (see Appendix C), suggesting
-the conjunction READ AND BOOK . Next, we investigate
-the sentence: YOU BETTER CHOOSE PAUL VERHOEVEN
-EVEN WATCHED . Since the label here is negative, we use
-theUNK token. We find that this prediction is brittle – a
-change of almost any word would be sufficient to flip the
-outcome. Anchors, on the other hand, reports a conjunction
-including most words in the sentence. Taking the R2I view,
-we still find a more concise explanation: CHOOSE orEVEN
-would be enough to attain a negative prediction. These brief
-examples illustrate how LENS may be used to find brittle
-predictions across samples, search for similarities between
-Figure 3: We compare PS(c;y)against precision scores at-
-tained by the output of LENS and Anchors for examples
-from German . We repeat the experiment for 100 inputs,
-and each time consider the single example generated by An-
-chors against the mean PS(c;y)among LENS’s candidates.
-Dotted line indicates = 0:9.
-errors, or test for model reliance on sensitive attributes (e.g.,
-gender pronouns).
-Anchors comparison. Anchors also includes a tabular
-variant, against which we compare LENS’s performance
-in terms of R2I sufficiency. We present the results of this
-comparison in Fig. 3, and include additional comparisons
-in Appendix C. We sample 100 inputs from the German
-dataset, and query both methods with = 0:9using the
-classifier from Sect. 5.1. Anchors satisfies a PAC bound
-controlled by parameter . At the default value = 0:1,
-Anchors fails to meet the threshold on 14% of samples;
-LENS meets it on 100% of samples. This result accords
-with Thm. 1, and vividly demonstrates the benefits of our
-optimality guarantee. Note that we also go beyond Anchors
-in providing multiple explanations instead of just a single
-output, as well as a cumulative probability measure with no
-analogue in their algorithm.
-5.3 COUNTERFACTUALS
-Adversarial examples: spam emails. R2I sufficiency an-
-swers questions of the form, “What would be sufficient
-for the model to predict y?”. This is particularly valuable
-in cases with unfavorable outcomes y0. Inspired by adver-
-sarial interpretability approaches [Ribeiro et al., 2018b;
-Lakkaraju and Bastani, 2020], we train an MLP classifier
-on the SpamAssassins dataset and search for minimal
-factors sufficient to relabel a sample of spam emails as non-
-spam. Our examples follow some patterns common to spam
-emails: received from unusual email addresses, includes sus-Table 2: Example prediction given by an LSTM model trained on the IMDB dataset. We compare -minimal factors identified
-by LENS (as individual words), based on PS(c;y)andPS(1c;1y), and compare to output by Anchors.
-Inputs Anchors LENS
-Text Original model prediction Suggested anchors Precision Sufficient R2I factors Sufficient I2R factors
-’read book forget movie’ wrongly predicted positive [read, movie] 0.94 [read, forget, movie] read, forget, movie
-’you better choose paul verhoeven even watched’ correctly predicted negative [choose, better, even, you, paul, verhoeven] 0.95 choose, even better, choose, paul, even
-Table 3: (Top) A selection of emails from SpamAssassins , correctly identified as spam by an MLP. The goal is to find
-minimal perturbations that result in non-spam predictions. (Bottom) Minimal subsets of feature-value assignments that
-achieve non-spam predictions with respect to the emails above.
-From To Subject First Sentence Last Sentence
-resumevalet info resumevalet com yyyy cv spamassassin taint org adv put resume back work dear candidate professionals online network inc
-jacqui devito goodroughy ananzi co za picone linux midrange com enlargement breakthrough zibdrzpay recent survey conducted increase size enter detailsto come open
-rose xu email com yyyyac idt net adv harvest lots target email address quickly want advertisement persons 18yrs old
-Gaming options Feature subsets for value changes
-From To
-1crispin cown crispin wirex com example com mailing... list secprog securityfocus... moderator
-From First Sentence
-2crispin cowan crispin wirex com scott mackenzie wrote
-From First Sentence
-3tim one comcast net tim peters tim
-picious keywords such as ENLARGEMENT orADVERTISE -
-MENT in the subject line, etc. We identify minimal changes
-that will flip labels to non-spam with high probability. Op-
-tions include altering the incoming email address to more
-common domains, and changing the subject or first sen-
-tences (see Table 3). These results can improve understand-
-ing of both a model’s behavior and a dataset’s properties.
-Diverse counterfactuals. Our explanatory measures can
-also be used to secure algorithmic recourse. For this experi-
-ment, we benchmark against DiCE [Mothilal et al., 2020b],
-which aims to provide diverse recourse options for any
-underlying prediction model. We illustrate the differences
-between our respective approaches on the Adult dataset
-[Kochavi and Becker, 1996], using an MLP and following
-the procedure from the original DiCE paper.
-According to DiCE, a diverse set of counterfactuals is
-one that differs in values assigned to features, and can
-thus produce a counterfactual set that includes different
-interventions on the same variables (e.g., CF1: age=
-91;occupation = “retired”; CF2: age= 44;occupation =
-“teacher”). Instead, we look at diversity of counterfactuals
-in terms of intervention targets , i.e. features changed (in
-this case, from input to reference values) and their effects.
-We present minimal cost interventions that would lead to re-
-course for each feature set but we summarize the set of paths
-to recourse via subsets of features changed. Thus, DiCE pro-
-vides answers of the form “Because you are not 91 and re-
-tired” or “Because you are not 44 and a teacher”; we answer
-“Because of your age and occupation”, and present the low-
-est cost intervention on these features sufficient to flip the
-prediction.
-With this intuition in mind, we compare outputs given by
-DiCE and LENS for various inputs. For simplicity, we let
-all features vary independently. We consider two metrics for
-comparison: (a) the mean cost of proposed factors, and (b)
-the number of minimally valid candidates proposed, where a
-Figure 4: A comparison of mean cost of outputs by LENS
-and DiCE for 50 inputs sampled from the Adult dataset.
-factorcfrom a method Misminimally valid iff for allc0pro-
-posed byM0,:(c0costc)(i.e.,M0does not report a fac-
-tor preferable to c). We report results based on 50 randomly
-sampled inputs from the Adult dataset, where references
-are fixed by conditioning on the opposite prediction. The
-cost comparison results are shown in Fig. 4, where we find
-that LENS identifies lower cost factors for the vast majority
-of inputs. Furthermore, DiCE finds no minimally valid can-
-didates that LENS did not already account for. Thus LENS
-emphasizes minimality anddiversity of intervention targets,
-while still identifying low cost intervention values.
-Causal vs. non-causal recourse. When a user relies on
-XAI methods to plan interventions on real-world systems,
-causal relationships between predictors cannot be ignored.
-In the following example, we consider the DAG in Fig. 5,
-intended to represent dependencies in the German credit
-dataset. For illustrative purposes, we assume access to the
-structural equations of this data generating process. (There
-are various ways to extend our approach using only partial
-causal knowledge as input [Karimi et al., 2020b; Heskes
-et al., 2020].) We construct Dby sampling from the SCM
-under a series of different possible interventions. Table 4
-describes an example of how using our framework with
-augmented causal knowledge can lead to different recourse
-options. Computing explanations under the assumption of
-feature independence results in factors that span a large
-part of the DAG depicted in Fig. 5. However, encoding
-structural relationships in D, we find that LENS assigns
-high explanatory value to nodes that appear early in the
-topological ordering. This is because intervening on a single
-root factor may result in various downstream changes once
-effects are fully propagated.Table 4: Recourse example comparing causal and non-causal (i.e., feature independent) D. We sample a single input
-example with a negative prediction, and 100 references with the opposite outcome. For I2R causal we propagate the effects
-of interventions through a user-provided SCM.
-input I2R I2Rcausal
-Age Sex Job Housing Savings Checking Credit Duration Purpose -minimal factors ( = 0)Cost-minimal factors ( = 0)Cost
-Job: Highly skilled 1 Age: 24 0.07
-Checking: NA 1 Sex: Female 1
-Duration: 30 1.25 Job: Highly skilled 1
-Age: 65, Housing: Own 4.23 Housing: Rent 123 Male Skilled Free Little Little 1845 45 Radio/TV
-Age: 34, Savings: N/A 1.84 Savings: N/A 1
-AgeSex
-JobSavingsHousingChecking
-CreditDuration
-Purpose
-Figure 5: Example DAG for German dataset.
-6 DISCUSSION
-Our results, both theoretical and empirical, rely on access to
-the relevant context Dand the complete enumeration of all
-feature subsets. Neither may be feasible in practice. When
-elements of Zare estimated, as is the case with the genera-
-tive methods sometimes used in XAI, modeling errors could
-lead to suboptimal explanations. For high-dimensional set-
-tings such as image classification, LENS cannot be naïvely
-applied without substantial data pre-processing. The first is-
-sue is extremely general. No method is immune to model
-misspecification, and attempts to recreate a data generat-
-ing process must always be handled with care. Empirical
-sampling, which we rely on above, is a reasonable choice
-when data are fairly abundant and representative. However,
-generative models may be necessary to correct for known
-biases or sample from low-density regions of the feature
-space. This comes with a host of challenges that no XAI al-
-gorithm alone can easily resolve. The second issue – that
-a complete enumeration of all variable subsets is often im-
-practical – we consider to be a feature, not a bug. Complex
-explanations that cite many contributing factors pose cog-
-nitive as well as computational challenges. In an influen-
-tial review of XAI, Miller [2019] finds near unanimous con-
-sensus among philosophers and social scientists that, “all
-things being equal, simpler explanations – those that cite
-fewer causes... are better explanations” (p. 25). Even if we
-could list all -minimal factors for some very large value of
-d, it is not clear that such explanations would be helpful to
-humans, who famously struggle to hold more than seven ob-
-jects in short-term memory at any given time [Miller, 1955].
-That is why many popular XAI tools include some sparsity
-constraint to encourage simpler outputs.
-Rather than throw out some or most of our low-level fea-
-tures, we prefer to consider a higher level of abstraction,where explanations are more meaningful to end users. For
-instance, in our SpamAssassins experiments, we started
-with a pure text example, which can be represented via
-high-dimensional vectors (e.g., word embeddings). How-
-ever, we represent the data with just a few intelligible com-
-ponents: From andToemail addresses, Subject , etc. In
-other words, we create a more abstract object and consider
-each segment as a potential intervention target, i.e. a candi-
-date factor. This effectively compresses a high-dimensional
-dataset into a 10-dimensional abstraction. Similar strategies
-could be used in many cases, either through domain knowl-
-edge or data-driven clustering and dimensionality reduction
-techniques [Chalupka et al., 2017; Beckers et al., 2019; Lo-
-catello et al., 2019]. In general, if data cannot be represented
-by a reasonably low-dimensional, intelligible abstraction,
-then post-hoc XAI methods are unlikely to be of much help.
-7 CONCLUSION
-We have presented a unified framework for XAI that fore-
-grounds necessity and sufficiency, which we argue are the
-fundamental building blocks of all successful explanations.
-We defined simple measures of both, and showed how they
-undergird various XAI methods. Our formulation, which re-
-lies on converse rather than inverse probabilities, is uniquely
-flexible and expressive. It covers all four basic explanatory
-measures – i.e., the classical definitions and their contra-
-positive transformations – and unambiguously accommo-
-dates logical, probabilistic, and/or causal interpretations, de-
-pending on how one constructs the basis tuple B. We illus-
-trated illuminating connections between our measures and
-existing proposals in XAI, as well as Pearl [2000]’s proba-
-bilities of causation. We introduced a sound and complete
-algorithm for identifying minimally sufficient factors, and
-demonstrated our method on a range of tasks and datasets.
-Our approach prioritizes completeness over efficiency, suit-
-able for settings of moderate dimensionality. Future research
-will explore more scalable approximations, model-specific
-variants optimized for, e.g., convolutional neural networks,
-and developing a graphical user interface.
-Acknowledgements
-DSW was supported by ONR grant N62909-19-1-2096.References
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-A PROOFS
-A.1 THEOREMS
-A.1.1 Proof of Theorem 1
-Theorem. With oracle estimates PS(c;y)for allc2C,
-Alg. 1 is sound and complete.
-Proof. Soundness and completeness follow directly from the
-specification of (P1) Cand (P2)in the algorithm’s input
-B, along with (P3) access to oracle estimates PS(c;y)for
-allc2C. Recall that the partial ordering must be complete
-and transitive, as noted in Sect. 3.
-Assume that Alg. 1 generates a false positive, i.e. outputs
-somecthat is not-minimal. Then by Def. 4, either the algo-
-rithm failed to properly evaluate PS(c;y), thereby violating
-(P3); or failed to identify some c0such that (i) PS(c0;y)
-and (ii)c0c. (i) is impossible by (P3), and (ii) is impos-
-sible by (P2). Thus there can be no false positives.
-Assume that Alg. 1 generates a false negative, i.e. fails to
-output some cthat is in fact -minimal. By (P1), this ccan-
-not exist outside the finite set C. Therefore there must besomec2Cfor which either the algorithm failed to properly
-evaluatePS(c;y), thereby violating (P3); or wrongly iden-
-tified somec0such that (i) PS(c0;y)and (ii)c0c.
-Once again, (i) is impossible by (P3), and (ii) is impossible
-by (P2). Thus there can be no false negatives.
-A.1.2 Proof of Theorem 2
-Theorem. With sample estimates ^PS(c;y)for allc2C,
-Alg. 1 is uniformly most powerful.
-Proof. A testing procedure is uniformly most powerful
-(UMP) if it attains the lowest type II error of all tests with
-fixed type I error . Let0;1denote a partition of the pa-
-rameter space into null and alternative regions, respectively.
-The goal in frequentist inference is to test the null hypoth-
-esisH0:20against the alternative H1:21for
-some parameter . Let (X)be a testing procedure of the
-form1[T(X)c], whereXis a finite sample, T(X)is a
-test statistic, and cis the critical value. This latter param-
-eter defines a rejection region such that test statistics inte-
-grate tounderH0. We say that  (X)is UMP iff, for any
-other test 0(X)such that
-sup
-20E[ 0(X)];
-we have
-(821)E[ 0(X)]E[ (X)];
-where E21[ (X)]denotes the power of the test to de-
-tect the true ,1 (). The UMP-optimality of Alg. 1
-follows from the UMP-optimality of the binomial test (see
-[Lehmann and Romano, 2005, Ch. 3]), which is used to de-
-cide between H0:PS(c;y)<  andH1:PS(c;y)
-on the basis of observed proportions ^PS(c;y), estimated
-fromnsamples for all c2C. The proof now takes the same
-structure as that of Thm. 1, with (P3) replaced by (P 30): ac-
-cess to UMP estimates of PS(c;y). False positives are no
-longer impossible but bounded at level ; false negatives
-are no longer impossible but occur with frequency . Be-
-cause no procedure can find more -minimal factors for any
-fixed, Alg. 1 is UMP.
-A.2 PROPOSITIONS
-A.2.1 Proof of Proposition 1
-Proposition. LetcS(z) = 1 iffxzwas constructed by
-holding xSfixed and sampling XRaccording toD(
jS).
-Thenv(S) =PS(cS;y).
-As noted in the text, D(xjS)may be defined in a variety of
-ways (e.g., via marginal, conditional, or interventional dis-
-tributions). For any given choice, let cS(z) = 1 iffxis con-
-structed by holding xS
-ifixed and sampling XRaccordingtoD(xjS). Since we assume binary Y(or binarized, as dis-
-cussed in Sect. 3), we can rewrite Eq. 2 as a probability:
-v(S) =PD(xjS)(f(xi) =f(x));
-where xidenotes the input point. Since conditional sam-
-pling is equivalent to conditioning after sampling, this value
-function is equivalent to PS(cS;y)by Def. 2.
-A.2.2 Proof of Proposition 2
-Proposition. LetcA(z) = 1 iffA(x) = 1 . Then
-prec(A) =PS(cA;y).
-The proof for this proposition is essentially identical, except
-in this case our conditioning event is A(x) = 1 . LetcA=
-1iffA(x) = 1 . Precision prec( A), given by the lhs of
-Eq. 3, is defined over a conditional distribution D(xjA).
-Since conditional sampling is equivalent to conditioning
-after sampling, this probability reduces to PS(cA;y).
-A.2.3 Proof of Proposition 3
-Proposition. Letcost be a function representing , and
-letcbe some factor spanning reference values. Then the
-counterfactual recourse objective is:
-c= argmin
-c2Ccost(c)s.t.PS(c;1y); (7)
-wheredenotes a decision threshold. Counterfactual out-
-puts will then be any zD such thatc(z) = 1 .
-There are two closely related ways of expressing the counter-
-factual objective: as a search for optimal points , or optimal
-actions . We start with the latter interpretation, reframing ac-
-tions as factors. We are only interested in solutions that flip
-the original outcome, and so we constrain the search to fac-
-tors that meet an I2R sufficiency threshold, PS(c;1y)
-. Then the optimal action is attained by whatever factor
-(i) meets the sufficiency criterion and (ii) minimizes cost.
-Call this factor c. The optimal point is then any zsuch that
-c(z) = 1 .
-A.2.4 Proof of Proposition 4
-Proposition. Consider the bivariate Boolean setting, as in
-Sect. 2. We have two counterfactual distributions: an input
-spaceI, in which we observe x;ybut intervene to set X=
-x0; and a reference space R, in which we observe x0;y0but
-intervene to set X=x. LetDdenote a uniform mixture
-over both spaces, and let auxiliary variable Wtag each sam-
-ple with a label indicating whether it comes from the origi-
-nal (W= 1) or contrastive ( W= 0) counterfactual space.
-Definec(z) =w. Then we have suf(x;y) =PS(c;y)and
-nec(x;y) =PS(1c;y0).Recall from Sect. 2 that Pearl [2000, Ch. 9] defines
-suf(x;y) :=P(yxjx0;y0)andnec(x;y) :=P(y0
-x0jx;y):
-We may rewrite the former as PR(y), where the reference
-spaceRdenotes a counterfactual distribution conditioned on
-x0;y0;do(x). Similarly, we may rewrite the latter as PI(y0),
-where the input space Idenotes a counterfactual distribu-
-tion conditioned on x;y;do (x0). Our contextDis a uniform
-mixture over both spaces.
-The key point here is that the auxiliary variable Windicates
-whether samples are drawn from IorR. Thus condition-
-ing on different values of Wallows us to toggle between
-probabilities over the two spaces. Therefore, for c(z) =w,
-we have suf(x;y) =PS(c;y)andnec(x;y) =PS(1
-c;y0).
-B ADDITIONAL DISCUSSIONS OF
-METHOD
-B.1-MINIMALITY AND NECESSITY
-As a follow up to Remark 2 in Sect. 3.2, we expand here
-upon the relationship between and cumulative probabili-
-ties of necessity, which is similar to a precision-recall curve
-quantifying and qualifying errors in classification tasks. In
-this case, as we lower , we allow more factors to be taken
-into account, thus covering more pathways towards a desired
-outcome in a cumulative sense. We provide an example of
-such a precision-recall curve in Fig. 6, using an R2I view of
-theGerman credit dataset. Different levels of cumulative
-necessity may be warranted for different tasks, depending on
-how important it is to survey multiple paths towards an out-
-come. Users can therefore adjust to accommodate desired
-levels of cumulative PN over successive calls to LENS.
-Figure 6: An example curve exemplifying the relationship
-betweenand cumulative probability necessity attained by
-selected-minimal factors.C ADDITIONAL DISCUSSIONS OF
-EXPERIMENTAL RESULTS
-C.1 DATA PRE-PROCESSING AND MODEL
-TRAINING
-German Credit Risk. We first download the dataset from
-Kaggle,3which is a slight modification of the UCI version
-[Dua and Graff, 2017]. We follow the pre-processing steps
-from a Kaggle tutorial.4In particular, we map the categori-
-cal string variables in the dataset ( Savings ,Checking ,
-Sex,Housing ,Purpose and the outcome Risk ) to nu-
-meric encodings, and mean-impute values missing values
-forSavings andChecking . We then train an Extra-Tree
-classifier [Geurts et al., 2006] using scikit-learn, with ran-
-dom state 0 and max depth 15. All other hyperparameters
-are left to their default values. The model achieves a 71%
-accuracy.
-German Credit Risk - Causal. We assume a partial order-
-ing over the features in the dataset, as described in Fig. 5.
-We use this DAG to fit a structural causal model (SCM)
-based on the original data. In particular, we fit linear regres-
-sions for every continuous variable and a random forest clas-
-sifier for every categorical variable. When sampling from
-D, we let variables remain at their original values unless ei-
-ther (a) they are directly intervened on, or (b) one of their
-ancestors was intervened on. In the latter case, changes are
-propagated via the structural equations. We add stochastic-
-ity via Gaussian noise for continuous outcomes, with vari-
-ance given by each model’s residual mean squared error.
-For categorical variables, we perform multinomial sampling
-over predicted class probabilities. We use the same fmodel
-as for the non-causal German credit risk description above.
-SpamAssassins. The original spam assassins dataset comes
-in the form of raw, multi-sentence emails captured on
-the Apache SpamAssassins project, 2003-2015.5We seg-
-mented the emails to the following “features”: From
-is the sender; Tois the recipient; Subject is the
-email’s subject line; Urls records any URLs found in
-the body; Emails denotes any email addresses found
-in the body; First Sentence ,Second Sentence ,
-Penult Sentence , andLast Sentence refer to the
-first, second, penultimate, and final sentences of the email,
-respectively. We use the original outcome label from the
-dataset (indicated by which folder the different emails were
-saved to). Once we obtain a dataset in the form above, we
-continue to pre-process by lower-casing all characters, only
-3See https://www.kaggle.com/kabure/
-german-credit-data-with-risk?select=german_
-credit_data.csv .
-4See https://www.kaggle.com/vigneshj6/
-german-credit-data-analysis-python .
-5Seehttps:
-//spamassassin.apache.org/old/credits.html .keeping words or digits, clearing most punctuation (except
-for ‘-’ and ‘_’), and removing stopwords based on nltk’s pro-
-vided list [Bird et al., 2009]. Finally, we convert all clean
-strings to their mean 50-dim GloVe vector representation
-[Pennington et al., 2014]. We train a standard MLP classi-
-fier using scikit-learn, with random state 1, max iteration
-300, and all other hyperparameters set to their default val-
-ues.6This model attains an accuracy of 98.3%.
-IMDB. We follow the pre-processing and modeling steps
-taken in a standard tutorial on LSTM training for sentiment
-prediction with the IMDB dataset.7The CSV is included in
-the repository named above, and can be additionally down-
-loaded from Kaggle or ai.standford.8In particular, these
-include removal of HTML-tags, non-alphabetical charac-
-ters, and stopwords based on the the list provided in the ntlk
-package, as well as changing all alphabetical characters to
-lower-case. We then train a standard LSTM model, with 32
-as the embedding dimension and 64 as the dimensionality
-of the output space of the LSTM layer, and an additional
-dense layer with output size 1. We use the sigmoid activa-
-tion function, binary cross-entropy loss, and optimize with
-Adam [Kingma and Ba, 2015]. All other hyperparameters
-are set to their default values as specified by Keras.9The
-model achieves an accuracy of 87.03%.
-Adult Income. We obtain the adult income dataset via
-DiCE’s implementation10and followed Haojun Zhu’s pre-
-processing steps.11For our recourse comparison, we use a
-pretrained MLP model provided by the authors of DiCE,
-which is a single layer, non-linear model trained with Ten-
-sorFlow and stored in their repository as ‘adult.h5’.
-C.2 TASKS
-Comparison with attributions. For completeness, we also
-include here comparison of cumulative attribution scores
-per cardinality with probabilities of sufficiency for the I2R
-view (see Fig. 7).
-Sentiment sensitivity analysis. We identify sentences in
-the original IMDB dataset that are up to 10 words long. Out
-of those, for the first example we only look at wrongly pre-
-dicted sentences to identify a suitable example. For the other
-6Seehttps://scikit-learn.org/stable/
-modules/generated/sklearn.\neural_network.
-MLPClassifier.html .
-7Seehttps://github.com/hansmichaels/
-sentiment-analysis-IMDB-Review-using-LSTM/
-blob/master/sentiment_analysis.py.ipynb .
-8See
-https://www.kaggle.com/lakshmi25npathi/
-imdb-dataset-of-50k-movie-reviews orhttp:
-//ai.stanford.edu/~amaas/data/sentiment/ .
-9Seehttps://keras.io .
-10Seehttps://github.com/interpretml/DiCE .
-11Seehttps://rpubs.com/H_Zhu/235617 .Table 5: Recourse options for a single input given by DiCE and our method. We report targets of interventions as suggested
-options, but they could correspond to different values of interventions. Our method tends to propose more minimal and
-diverse intervention targets. Note that all of DiCE’s outputs are already subsets of LENS’s two top suggestions, and due to
--minimality LENS is forced to pick the next factors to be non-supersets of the two top rows. This explains the higher cost
-of LENS’s bottom three rows.
-input DiCE output LENS output
-Age Wrkcls Edu. Marital Occp. Race Sex Hrs/week Targets of intervention Cost Targets of intervention Cost
-Age, Edu., Marital, Hrs/week 8.13 Edu. 1
-Age, Edu., Marital, Occp., Sex, Hrs/week 5.866 Martial 1
-Age, Wrkcls, Educ., Marital, Hrs/week 5.36 Occp., Hrs/week 19.3
-Age, Edu., Occp., Hrs/week 3.2 Wrkcls, Occp., Hrs/week 12.642 Govt. HS-grad Single Service White Male 40
-Edu., Hrs/week 11.6 Age, Wrkcls, Occp., Hrs/week 12.2
-Figure 7: Comparison of degrees of sufficiency in I2R set-
-ting, for top kfeatures based on SHAP scores, against the
-best performing subset of cardinality kidentified by our
-method. Results for German are averaged over 50 inputs;
-results for SpamAssassins are averaged over 25 inputs.
-example, we simply consider a random example from the
-10-word maximum length examples. We noted that Anchors
-uses stochastic word-level perturbations for this setting. This
-leads them to identify explanations of higher cardinality for
-some sentences, which include elements that are not strictly
-necessary. In other words, their outputs are not minimal, as
-required for descriptions of “actual causes” [Halpern and
-Pearl, 2005a; Halpern, 2016].
-Comparison with Anchors. To complete the picture of
-our comparison with Anchors on the German Credit Risk
-dataset, we provide here additional results. In the main text,
-we included a comparison of Anchors’s single output preci-
-sion against the mean degree of sufficiency attained by our
-multiple suggestions per input. We sample 100 different in-
-puts from the German Credit dataset and repeat this same
-comparison. Here we additionally consider the minimum
-and maximum PS(c;y)attained by LENS against Anchors.
-Note that even when considering minimum PSsuggestions
-by LENS, i.e. our worst output, the method shows more con-
-sistent performance. We qualify this discussion by noting
-that Anchors may generate results comparable to our own
-by setting the hyperparameter to a lower value. However,
-Ribeiro et al. [2018a] do not discuss this parameter in de-
-tail in either their original article or subsequent notebook
-guides. They use default settings in their own experiments,
-and we expect most practitioners will do the same.
-Recourse: DiCE comparison First, we provide a single
-Figure 8: We compare degree of sufficiency against preci-
-sion scores attained by the output of LENS and Anchors for
-examples from German . We repeat the experiment for 100
-sampled inputs, and each time consider the single output
-by Anchors against the min (left) and max (right) PS(c;y)
-among LENS’s multiple candidates. Dotted line indicates
-= 0:9, the threshold we chose for this experiment.
-illustrative example of the lack of diversity in intervention
-targets we identify in DiCE’s output. Let us consider one
-example, shown in Table 5. While DiCE outputs are diverse
-in terms of values and target combinations, they tend to
-have great overlap in intervention targets. For instance, Age
-andEducation appear in almost all of them. Our method
-would focus on minimal paths to recourse that would involve
-different combinations of features.
-Figure 9: We show results over 50 input points sampled
-from the original dataset, and all possible references of the
-opposite class, across two metrics: the min cost (left) of
-counterfactuals suggested by our method vs. DiCE, and the
-max cost (right) of counterfactuals.
-Next, we also provide additional results from our cost com-
-parison with DiCE’s output in Fig. 8. While in the main text
-we include a comparison of our mean cost output against
-DiCE’s, here we additionally include a comparison of min
-and max cost of the methods’ respective outputs. We see thateven when considering minimum and maximum cost, our
-method tends to suggest lower cost recourse options. In par-
-ticular, note that all of DiCE’s outputs are already subsets of
-LENS’s two top suggestions. The higher costs incurred by
-LENS for the next two lines are a reflection of this fact: due
-to-minimality, LENS is forced to find other interventions
-that are no longer supersets of options already listed above.

txt/2104.03109.txt

@@ -1,919 +0,0 @@
-VGF-Net: Visual-Geometric Fusion Learning
-for Simultaneous Drone Navigation and Height Mapping
-Yilin Liu
-Shenzhen UniversityKe Xie
-Shenzhen UniversityHui Huang*
-Shenzhen University
-Abstract
-The drone navigation requires the comprehensive un-
-derstanding of both visual and geometric information
-in the 3D world. In this paper, we present a Visual-
-Geometric Fusion Network (VGF-Net), a deep network
-for the fusion analysis of visual/geometric data and
-the construction of 2.5D height maps for simultaneous
-drone navigation in novel environments. Given an initial
-rough height map and a sequence of RGB images, our
-VGF-Net extracts the visual information of the scene,
-along with a sparse set of 3D keypoints that capture
-the geometric relationship between objects in the scene.
-Driven by the data, VGF-Net adaptively fuses visual
-and geometric information, forming a unified Visual-
-Geometric Representation . This representation is fed to
-a new Directional Attention Model (DAM), which helps
-enhance the visual-geometric object relationship and
-propagates the informative data to dynamically refine
-the height map and the corresponding keypoints. An
-entire end-to-end information fusion and mapping sys-
-tem is formed, demonstrating remarkable robustness
-and high accuracy on the autonomous drone navigation
-across complex indoor and large-scale outdoor scenes.
-1. Introduction
-In recent years, we have witnessed the development of
-autonomous robotic systems that have been broadly used
-in many scenarios (e.g., autonomous driving, manufactur-
-ing and surveillance). Drone belongs to the robotic system,
-and is well-known for its flying capacity. Navigation is ex-
-tremely important to the drone fly, as it facilitates the effec-
-tive exploration and recognition of the unknown environ-
-ments. Yet, the navigation of drone remains a challenging
-task, especially for planning the pathway as short as pos-
-sible to the target/destination whilst avoiding the potential
-collision with objects in the unexplored space. The conven-
-tional navigation heavily relies on the expertise of human,
-who intuitively designs the drone flyby trajectory based on
-the spatial layout within the visible range. The resulting
-*Corresponding author: Hui Huang ([email protected])
-Figure 1: We show a drone navigation trajectory (yellow
-curve) in 3D scene, which connects the starting and target
-points (red dots). During the navigation, our VGF-Net dy-
-namically updates the 2.5D height map (see the bottom-left
-corner) in new places (see pictures in red rectangles), which
-is used to timely update the navigation trajectory.
-navigation system lacks of the globe knowledge of scenes,
-leading to unsatisfactory or even failed path planning.
-To better leverage the global information of 3D en-
-vironment, researches on drone navigation have focused
-on collecting and memorizing the environmental informa-
-1arXiv:2104.03109v1  [cs.CV]  7 Apr 2021tion during the navigating process. Typically, the exist-
-ing works [11, 2, 3] employ the mapping techniques to
-construct 2D/3D maps with respect to the vacant/occupied
-space. The mapping result contains rich geometric relation-
-ship between objects, which helps to navigate. There have
-also been navigation approaches based on visual informa-
-tion [6, 10, 2], saving the computational overhead to con-
-struct maps. Nonetheless, these works purely condition the
-accuracy of navigation on either geometric or visual infor-
-mation.
-In this paper, we utilize 2.5D height map for autonomous
-drone navigation. There are growing computer applica-
-tions that use height map to represent the boundaries of
-objects (e.g., buildings or furniture). Nonetheless, there is
-nothing guaranteed for the quality of given height maps,
-as the mapping process likely involves incomplete or out-
-of-date information. Here, we advocate the importance
-of fusing geometric and visual information for a more ro-
-bust construction of the height map. The new trend of re-
-searches [24, 5] on the 3D object/scene understanding has
-also demonstrated that the geometric relationship between
-objects and visual appearance of scenes are closely corre-
-lated. We thus propose a Visual-Geometric Fusion Network
-(VGF-Net) to dynamically update the height map during
-drone navigation by utilizing the timely captured new im-
-ages (see Figure 1).
-More specifically, as illustrated in Figure 2, the network
-takes an initial rough height map together with a sequence
-of RGB images as input. We use convolutional layers to
-compute the visual and geometric information to renew the
-height map. Next, we apply the simultaneous localization
-and mapping (SLAM) [20] module to extract a sparse set
-of 3D keypoints from the image sequence. These key-
-points are used along with the renewed height map to con-
-struct a novel Visual-Geometric Representation , which is
-passed to a Directional Attention Model . This attention
-model exchanges visual and geometric information among
-objects in the scene, providing quite useful object relation-
-ship for simultaneous refinement of the height map and
-the corresponding keypoints, leading to the successful path
-planning [15] at each navigation moment. Compared to
-dense point clouds that require time-consuming depth es-
-timation [4] and costly processing, the sparse keypoints we
-use are fast to compute yet effective in terms of capturing
-useful geometric information without much redundancy. As
-the drone flies over more and more places, our network can
-achieve and fuse more and more the visual and geometric
-information to largely increase the precision of height map
-and consequently the reliability of autonomous navigation.
-We intensively train and evaluate our method on a bench-
-mark of seven large-scale urban scenes and six complex
-indoor scenes for height map construction and drone nav-
-igation. The experimental results and comparative statisticsclearly demonstrate the effectiveness and the robustness of
-our proposed VGF-Net.
-2. Related Work
-There have been an array of researches on the naviga-
-tion system that allows robots to smartly explore the real
-world. Below, we will mainly survey on the drone naviga-
-tion and environment mapping, as they are highly relevant
-to our work in the sense that their navigation systems are
-driven by the critical environment data.
-2.1. Drone Navigation
-The modern drone systems are generally equipped with
-various sensors (e.g., RGB-D camera, radar and GPS),
-which help the hardware devices to achieve accurate percep-
-tion of the real world. Typically, the data captured by sen-
-sors is used for mapping (i.e., the construction of map), pro-
-viding comprehensive information for planning the moving
-path of drone. During the navigation process, the traditional
-methods [11, 22] compute the trajectory of drone based on
-the pre-scribed maps. However, the construction of a pre-
-cise map is generally expensive and time-consuming. Thus,
-the recent works [6, 10, 2] simplify the construction of map
-to facility more commercially-cheap navigation.
-The advances on deep learning have significantly im-
-proved the robustness of visual navigation, leading to the
-emergency of many navigation systems that do not rely on
-the given maps. Kim et al. [14] and Padhy et al. [21] use the
-classification neural network to predict the direction (e.g.,
-right, left or straight) of moving drone. Furthermore, Lo-
-quercio et al. [17] and Mirowski et al. [19] use neural net-
-works to compute the angle of flying and the risk of colli-
-sion, which provide more detailed information to control
-the drone flyby. Note that the above methods learn the
-actions of drone from the human annotations. The latest
-works employ deep reinforcement learning [23, 29, 26] to
-optimize the network, enabling more flexible solutions for
-autonomous drone navigation in novel environments.
-Our approach utilizes a rough 2.5D height map to in-
-crease the success rate of navigation in different complex
-scenes, which may have various spatial layouts of objects.
-Compared to the existing methods that conduct the mapping
-before navigation, we allow for real-time intelligent update
-of the height map during navigation, largely alleviating neg-
-ative impacts of problematic mapping results.
-2.2. Mapping Technique
-The mapping technique is fundamental in the drone nav-
-igation. The techniques of 2D mapping have been widely
-used in the navigation task. Henriques et al. [11] and Savi-
-nov et al. [22] use 2D layout map to store useful informa-
-tion, which is learned by neural networks from the imageOffset... ...
-Projection
-SLAM
-ConvConv
-Direational Attention ModelKeypoints
-t
-1
-tMt+1rM tcM trR tcR
-Figure 2: Overview of VGF-Net. At the tthmoment, the network uses convolutional layers to learn visual and geometric
-representations from the RGB image Itand 2.5D height map Mt(produced at the (t1)thmoment). The representations
-are combined to compute the residual update map Rc
-t, which is added to the 2.5D height map to form a renewed height
-mapMc
-t. Based on the new height map and the 3D keypoints fpt;1; :::; p t;Ng(produced by SLAM), we construct the VG
-representation for each keypoint (yellow dot), which is used by DAM to select useful information to refine object boundaries
-and 3D keypoints at the next moment. Note that the refined height map Mr
-t+1is used for path planning, which is omitted for
-a simple illustration.
-data of 3D scenes. Chen et al. [6] use the 2D topologi-
-cal map, which can be constructed using the coarse spatial
-layout of objects, to navigate the robot in an indoor scene.
-Different from the methods that consider the 2D map of an
-entire scene, Gupta et al. [10] unify the mapping and 2D
-path planning to rapidly adjust the navigation with respect
-to the surrounding local environment. Bansal et al. [2] uti-
-lize sparse waypoints to represent the map, which can be
-used to generate a smooth pathway to the target object or
-destination.
-Compared to 2D mapping, 3D mapping provides much
-richer spatial information for the navigation system. Wang
-et al. [27] use visual odometry to capture the geometric re-
-lationship between 3D points, which is important to recon-
-struct the 3D scene. Engel et al. [8, 7] integrate the tracking
-of keypoints into the mapping process, harnessing tempo-
-ral information to produce a more consistent mapping of
-the global environment. Futhermore, Huang et al. [12, 13]
-use a probabilistic Conditional Random Field model and a
-noise-aware motion affinity matrix to effectively track both
-moving and static objects. Wang et al. [28] use plane as
-a geometric constrain to reconstruct the whole scene. Be-
-sides 3D points, depth information is also important to 3D
-mapping. During the mapping process, Tateno et al. [25]
-and Ma et al. [18] use neural networks to estimate the depth
-map of a single image, for a faster construction of the 3D
-map. However, the fidelity of depth estimation is bounded
-by the scale of training data. To enhance, Kuznietsov et
-al. [16], Godard et al. [9] and Bian et al. [3] train the depthestimation network in semi-supervised/unsupervised man-
-ner, where the consistence in-between images are learned.
-Nowadays, a vast of real-world 3D models and applica-
-tions emerge, such as Google earth, and so there is abundant
-data of height maps available for the training of drone nav-
-igation system. Nonetheless, the accuracy and timeliness
-of such data is impossible to be guaranteed, thus hard to
-be directly used in practice. We deeply exploit the visual-
-geometric information fusion representation to effectively
-and dynamically update the given height map during navi-
-gation, yielding a significant increase of the success rate of
-the autonomous drone navigation in various novel scenes.
-3. Overview
-The core idea behind our approach is to fuse the visual
-and geometric information for the construction of height
-map. This is done by our Visual-Geometric Fusion Net-
-work (VGF-Net) to compute the visual-geometric represen-
-tation with respect to the visual and geometric consistence
-between the 3D keypoints and object boundaries character-
-ized in the height map. VGF-Net uses the fused representa-
-tion to refine the keypoints and height map at each moment
-during drone navigation. Below, we outline the architecture
-of VGF-Net.
-As illustrated in Figure 2, at the tthmoment ( t0), the
-network takes the RGB image Itand the associated height
-mapMtas input. The image Itis fed to convolutional lay-
-ers to compute the visual representation Vt. The height map
-Mtis also input to the convolutional layers for the geomet-ric representation Gt. The visual and geometric representa-
-tions are fused to compute the residual update map Rc
-tthat
-updates the height map to Mc
-t, providing more consistent
-information for the subsequent steps.
-Next, we use the SLAM [20] module to compute a sparse
-set of 3D keypoints fpt;1; :::; p t;Ng, based on the images
-fI1; :::; I tg. We project these keypoints to the renewed
-height map Mc
-t. For the keypoint pt;i, we compute a set
-of distancesfdt;i;1; :::; d t;i;Kg, where dt;i;kdenotes the dis-
-tance from the keypoint pt;ito the nearest object boundary
-along the kthdirection (see Figure 3(a)). Intuitively, the
-keypoint, which is extracted around the objects in the 3D
-scene, is also near to the boundaries of the corresponding
-objects in the height map. This relationship between the
-keypoint pt;iand the object can be represented by the vi-
-sual and geometric information in the scene. Specifically,
-this is done by fusing the visual representation Vt, geomet-
-ric representation Gc
-t(learned from the renewed height map
-Mc
-t) and the distances fdt;i;1; :::; d t;i;Kgto form a novel
-Visual-Geometric (VG) representation Uifor the keypoint
-pt;i. For all keypoints, we compute a set of VG representa-
-tionsfUt;1; :::; U t;Ng.
-Finally, we employ a Directional Attention Model
-(DAM), which takes input as the VG representations
-fUt;1; :::; U t;Ng, to learn a residual update map Rr
-tto refine
-the height map Mc
-t. The DAM produces a new height map
-Mr
-t+1that respects the importance of each keypoint to the
-object boundaries in different directions (see Figure 3(b)).
-Meanwhile, we use DAM to compute a set of spatial off-
-setsf
pt+1;1; :::;
pt+1;Ngto update the keypoints, whose
-locations are imperfectly estimated by the SLAM. We use
-the height map Mr
-t+1for dynamic path planning [15] at the
-(t+ 1)thmoment, and meanwhile input the image It+1and
-the height map Mr
-t+1to VGF-Net at this moment for next
-update. As drone flies, the network achieves more accu-
-rate information and works more robustly for simultaneous
-drone navigation and height mapping.
-4. Method
-We now introduce our VGF-Net in more detail. The net-
-work extracts visual and geometric information from the
-RGB images, the associated 2.5D height map and 3D key-
-points. In what follows, we formally define the informa-
-tion fusion that produces the visual-geometric representa-
-tion, which is then used for the refinement of the height
-map and keypoints.
-4.1. Residual Update Strategy
-The VGF-Net refines the height map and keypoints iter-
-atively, as the drone flies to new places and captures new
-images. We divide this refinement process into separate
-moments. At the tthmoment, we feed the RGB image
-It2RHIWI3and the height map Mt2RHMWMinto the VGF-Net, computing the global visual represen-
-tation Vt2RHMWMCand the geometric representation
-Gt2RHMWMCas:
-Vt=Fv(It); G t=Fg(Mt); (1)
-whereFvandFgdenote the two sets of convolutional lay-
-ers. Note that the value of each location on Mtrepresents
-the height of object, and we set the height of ground to be 0.
-We concatenate the representations VtandGtfor comput-
-ing a residual update map Rc
-t2RHMWM, which is used
-to update the height map Mtas:
-Mc
-t=Mt+Rc
-t; (2)
-where
-Rc
-t=Fc(Vt; Gt): (3)
-Here, Mc
-t2RHMWMis a renewed height map, and Fc
-denotes a set of convolutional layers. Compared to directly
-computing a new height map, the residual update strategy
-(as formulated by Eq. (2)) adaptively reuses the information
-ofMt. More importantly, we learn the residual update map
-Rc
-tfrom the new content captured at the tthmoment. It
-facilitates a more focused update on the height values of
-regions that are unexplored before the tthmoment. The
-height map Mc
-tis fed to an extra set of convolutional layers
-to produce the representation Gc
-t, which will be used for the
-construction of the visual-geometric representation.
-4.2. Visual-Geometric Representation
-We conduct the visual-geometric information fusion
-to further refine the height map. To capture the geo-
-metric relationship between objects, we use a standard
-SLAM [20] module to extract a sparse set of 3D keypoints
-fpt;1; :::; p t;Ngfrom the sequence of images fI1; :::; I tg.
-Given the keypoint pt;i2R13in the camera coordinate
-system, we project it to the 2.5D space as:
-p0
-t;i=pt;iSR+T: (4)
-Here, S2R33is decided by a pre-defined scale factor,
-which could be calculated at the initialization of the SLAM
-system or by GPS adjustment. T2R13andR2R33
-translate the origin of the 3D point set from the camera to
-the height map coordinate system. In the height map co-
-ordinate system, the drone is located at (W
-2;0), where W
-represent the width of the height map.
-Note that the first two dimensions of p0
-t;i2R13indi-
-cate the location on the height map, and the third dimension
-indicates the corresponding height value. The set of key-
-pointsfp0
-t;1; :::; p0
-t;Ngare used for constructing the visual-
-geometric representations.
-Next, for each keypoint p0
-t;i, we compute its distances to
-the nearest objects in Kdifferent directions. Here, we refer(a) VG Representation (b) DAM
-height increase height decrease
-Figure 3: Illustration of fusing visual and geometric infor-
-mation for updating the 2.5D height map. (a) We construct
-the VG representation for each 3D keypoint (yellow dot)
-projected to the 2.5D height map. The information of VG
-representation is propagated to surrounding object bound-
-aries, along different directions (indicated by different col-
-ors). The distance between the keypoint and object bound-
-ary (black arrow) determines the weight for adjusting the in-
-formation propagation. The dash arrow means that there is
-no object along the corresponding direction. (b) Given the
-existing object boundary, we use DAM to select the most
-relevant keypoint along each direction. We use the selected
-keypoints to provide fused visual and geometric informa-
-tion, which is used for refining object boundary.
-to objects as the regions that have larger height values than
-the ground (with height value of 0) in the height map Mc
-t.
-As illustrated in Figure 3(a), we compute the Euclidean dis-
-tance dt;i;kalong the kthdirection, from p0
-t;ito the first lo-
-cation, where the height value is larger than 0. We compute
-a set of distancesfdt;i;1; :::; d t;i;KgforKdirections, then
-useVt(see Eq. (1)), Gc
-tand this distance set to form the VG
-representation Ut;i2RKas:
-Ut;i;k=Fv
-k(Wt;i;kVt) +Fg
-k(Wt;i;kGc
-t;i); (5)
-where
-Wv
-t;i;k=KX
-k0=1exp(jdt;i;kdt;i;k0j): (6)
-Here, Gc
-t;i2RCdenotes the feature vectors located in p0
-t;i
-in the map Gc
-t. In Eq. (5), Ut;i;kis represented as a weighted
-map with the resolution equal to the geometric representa-
-tion ( 2020by default), where Wt;i;kplays as a weight of
-importance that is determined by the distance from the key-
-pointp0
-t;ito the nearest object boundary along the kthdirec-
-tion. As formulated in Eq. (5) and Eq. (6), longer distance
-decays the importance. Besides, we use independent set of
-fully connected layers (i.e., Fv
-kandFg
-kin Eq. (5)) to learn
-important information from VtandGc
-t;i. It allows the con-
-tent, which is far from p0
-t;i, to have the opportunity to makean impact on Ut;i;k. We construct the VG representation for
-each keypoint infp0
-t;1; :::; p0
-t;Ng, while each VG represen-
-tation captures the visual and geometric information around
-the corresponding keypoint. Based on the the VG represen-
-tations, we propagate the information of the keypoints to
-each location on the height map, where the corresponding
-height value is refined. We also learn temporal information
-from the VG representations to refine the spatial locations
-of keypoints at the (t+ 1)thmoment, as detailed below.
-4.3. Directional Attention Model
-We use DAM to propagate the visual and geometric
-information, from each keypoint to each location on the
-height map, along different directions. More formally, for
-a location ph
-j2R13on the height map Mc
-t, we conduct
-the information propagation that yields a new representation
-Qt;j2RCKas:
-Qt;j=NX
-i=1Gc
-t;jU>
-t;i: (7)
-Along the second dimension of the representation Qt;j, we
-perform max pooling to yield Q0
-t;j2RCas:
-Q0
-t;j;c= max( Qt;j;c; 1; :::; Q t;j;c;K ): (8)
-As illustrated in Eq. (7), Qt;j;c;k summarizes the influence
-of all keypoints along kthdirection. We perform max pool-
-ing on the setfQt;j;c; 1; :::; Q t;j;c;Kg(see Eq. (8)), attend-
-ing to the most information along a direction to form the
-representation Q0
-t;j;c(see Figure 3(b)). To further refine the
-height map, we use the representation Q0
-t2RHMWMC
-to compute another residual update map Rr
-t2RHMWM,
-which is added to the height map Mc
-tto form a new height
-mapMr
-t+12RHMWMas:
-Mr
-t+1=Mc
-t+Rr
-t; (9)
-where
-Rr
-t=Fr(Vt; Q0
-t): (10)
-Again,Frdenotes a set of convolutional layers. We make
-use of the new height map Mr
-t+1for the path planning at the
-(t+ 1)thmoment.
-We refine not only the 2.5D height map but also
-the 3D keypoints at the (t+ 1)thmoment. Assume
-that we use SLAM to produce a new set of keypoints
-fpt+1;1; :::; p t+1;Ng. We remark that the keypoint sets at
-thetthand(t+ 1)thmoments are not necessary the same.
-To refine the new keypoint pt+1;j2R13, we use DAM to
-compute the representation 
p0
-t+1;j2R3Kas:
-
p0
-t+1;j=NX
-i=1pt;iU>
-t;i: (11)Figure 4: Overview of our 3D urban navigation dataset, including 7 city scenes with different characteristics.
-In this way, DAM distills the information of keypoints
-at the tthmoment, which is propagated to the next mo-
-ment. Again, we use max pooling to form the spatial offset
-
pt+1;j;c2R13for updating keypoint pt+1;jas:
-
pt+1;j;c= max(
 p0
-t+1;j;c; 1; :::;
p0
-t+1;j;c;K ):(12)
-We take the average of the updated keypoints pt+1;j+
-
pt+1;jand the estimated keypoints pt+1;jin place of
-the original one to construct the VG representation at the
-(t+ 1)thmoment.
-4.4. Training Details
-We use the L1loss function for training the VGF-Net as:
-L(Mgt
-t; Mr
-t) =TX
-t=1HWX
-j=1jMgt
-t;jMr
-t;jj; (13)
-where Mgt
-tRHWis the ground-truth height map. Ac-
-tually, we select 8 pairs of RGB image and height map
-(T= 8) to construct each mini-batch for the standard SGD
-solver. We set the height and width of each RGB image
-(224224) and the height map ( 2020). The overall train-
-ing samples is nearly 24000 images randomly sampled in 3
-scenes, while we test the model on the 24000 samples sam-
-pled on the other 3 scenes. Details about the dataset could
-be found in Sec. 5. We train the network for 30 epochs,
-and use the final snapshot of network parameters for test-
-ing. The learning rate is set to 0.001 at the first 15 epochs,
-and decayed to 0.0001 for a more stable optimization.
-By default, the backbone of FvandFgis a ResNet-18,
-while the remained FcandFris two stacked 33convo-
-lutional layer with max-pooling and batch normalization.Note that it is our contribution to learn spatial offsets
-of 3D keypoints, without explicitly using any ground-truth
-data. This is done by modeling the computation of spatial
-offsets as a differentiable function with respect to the VG
-representation. In this way, we enable the end-to-end learn-
-ing of spatial offsets, where the related network parameters
-can be optimized by the back-propagated gradients. It sig-
-nificantly reduces the effort for data annotation, while al-
-lows the network training to be flexibly driven by data.
-When constructing the VG representation, we set the
-number of directions K= 16 for each keypoint, and the
-number of keypoints N= 50 at each moment. We remark
-that these hyper-parameters are chosen based on the valida-
-tion results.
-5. Results and Discussion
-5.1. Description of Experimental Dataset
-To promote the related research on drone navigation, we
-newly collect a 3D urban navigation dataset. This dataset
-contains 7 models of different city scenes (see Figure 4).
-Note that New York, Chicago, San Francisco, and Las
-Vegas are Google Earth models we download, which are
-similar to the real-world scenes with respect to the appear-
-ance but most objects inside are only buildings. We have
-also Shenzhen, Suzhou and Shanghai that are manually built
-based on the map by professional modelers, which contain
-rich 3D objects (e.g., buildings, trees, street lights and road
-signs, etc.) and other stuff (e.g., ground, sky and sea). There
-are various spatial configurations of objects, building styles
-and weather conditions in these 3D scenes. Thus, we pro-
-vide challenging data for evaluating the navigation system.Table 1: Statistics of our 3D urban navigation dataset. Note that in addition to buildings, there may also exist many other
-objects we must consider, such as trees, flower beds, and street lights, which highly increase the challenge for height mapping
-and autonomous navigation task.
-scene area (km2)objects (#) model size ( MB )texture images (#) texture size ( MB )
-New York 7.4 744 86.4 762 122
-Chicago 24 1629 146 2277 227
-San Francisco 55 2801 225 2865 322
-Las Vegas 20 1408 108 1756 190
-Shenzhen 3 1126 50.3 199 72.5
-Suzhou 7 168 191 395 23.7
-Shanghai 37 6850 308 2285 220
-Table 2: Comparisons with different strategies of information fusion, in terms of the accuracy of height mapping (average L1
-error). We also show the accuracies ( %) of predicting height values, with respect to different ranges of error ( <3m, 5mand
-10m). All strategies are evaluated on the testing (i.e., unknown and novel) scenes of San Francisco, Shenzhen and Chicago.
-methodaverage L1error ( m) accuracy w.r.t. error 2[0;3]m(%)
-San Francisco Shenzhen Chicago San Francisco Shenzhen Chicago
-w/o fusion 4.57 4.57 4.49 68.95% 68.02% 70.05%
-w/ fusion 2.37 2.93 3.41 85.09% 83.63% 78.44%
-w/ fusion and memory 2.81 3.44 4.02 79.86% 79.20% 72.86%
-w/ fusion, memory and exchange 2.35 3.04 3.80 80.54% 82.36% 74.73%
-full strategy 1.98 2.72 3.10 85.71% 86.13% 80.46%
-methodaccuracy w.r.t. error 2[0;5]m(%) accuracy w.r.t. error 2[0;10]m(%)
-San Francisco Shenzhen Chicago San Francisco Shenzhen Chicago
-w/o fusion 75.02% 74.08% 76.86% 83.96% 83.96% 85.71%
-w/ fusion 89.20% 87.39% 84.12% 93.87% 92.25% 91.18%
-w/ fusion and memory 86.35% 84.56% 80.36% 93.00% 91.31% 89.51%
-w/ fusion, memory and exchange 86.13% 86.43% 81.41% 93.33% 91.85% 89.94%
-full strategy 89.22% 88.90% 85.30% 94.10% 92.56% 91.67%
-The models are input to the render for producing sequences
-of RGB images. All RGB images and the associated 2.5D
-height maps are used to form a training set (i.e., New York,
-Las Vegas and Suzhou) and a testing set (i.e., San Francisco,
-Shenzhen, and Chicago). We provides more detailed statis-
-tics of the dataset in Table 1.To train our VGF-Net, which takes as input a rough im-
-perfect height map and outputs an accurate height map,
-we use 5 types of manipulations (i.e., translation, height
-increase/decrease, size dilation/contraction, creation and
-deletion) to disturb the object boundaries in the ground-
-truth height map. One time of the disturbance increases orBefore disturbance After disturbance Residual map050100
-Translation + Dilation
-DilationHeight increaseHeight decrease
-Translation
-TranslationCreationDeletion
-Figure 5: Illustration of disturbance manipulations. Actu-
-ally, these manipulations can be combined to yield the dis-
-turbance results (e.g., translation and dilation). The bottom
-row of this figure shows the difference between height maps
-before/after disturbance. The residual map is learned by our
-VGF-Net, for recovering the disturbed height map to the
-undisturbed counterpart.
-decreases height values by 10 min certain map locations.
-See Figure 5 for an illustration of our manipulations.
-5.2. Different Strategies of Information Fusion
-The residual update, VG representation and DAM are
-critical components of VFG-Net, defining the strategy of
-information fusion. Below, we conduct an internal study by
-removing these components, and examine the effect on the
-accuracy of height mapping (see Table 2).
-First, we report the performance using visual informa-
-tion only for height mapping, disabling any visual and geo-
-metric fusion. Here, the visual information is learned from
-RGB images (see the entries “w/o fusion” in Table 2). But
-visual information is insufficient for reconstructing height
-maps, which requires the modeling of geometric relation-
-ship between objects, yielding lower performances com-
-pared to other methods using geometric information.
-Next, we examine the efficiency of residual update strat-
-egy. At each moment, the residual update allows VGF-Net
-to reuse the mapping result produced earlier. This strategy,
-where the useful visual and geometric contents can be ef-
-fectively distilled and memorized at all moments, improves
-the reliability of height mapping. Thus, by removing the
-residual update (see the entries “w/ fusion” in Table 2) from
-VGF-Net (see the entries “full strategy”), we degrade the
-performance of height mapping.
-We further study the effect of VG representation on the
-performance. The VG representation can be regarded as an
-information linkage. It contains fused visual and geometric
-information, which is exchanged among objects. Withoutthe VG representation, we use independent sets of convo-
-lutional layers to extract the visual and geometric represen-
-tations from the image and height map, respectively. The
-representations are simply concatenated for computing the
-residual update map (see the entries “w/ fusion and mem-
-ory” in Table 2). This manner successfully disconnects the
-communication between objects and leads to performance
-drops on almost all scenes, compared to our full strategy of
-information fusion.
-We find that the performance of using memory of height
-values lags behind the second method without using mem-
-ory (see the entries “w/ fusion” in Table 2). We explain that
-the information fusion with memory easily accumulates er-
-rors in the height map over time. Thus, it is critical to com-
-pute the VG representation based on the memorized infor-
-mation, enabling the information exchange between objects
-(see the entries “w/ fusion, memory and exchange”). Such
-exchange process provides richer object relationship to ef-
-fectively address the error accumulation problem, signifi-
-cantly assisting height mapping at each moment.
-Finally, we investigate the importance of DAM (see the
-entries “w/ fusion, memory and exchange” in Table 2). We
-solely remove DAM from the full model, by directly us-
-ing VG representations to compute the residual update map
-and spatial offsets for refining the height map and key-
-points. Compared to this fusion strategy, our full strategy
-with DAM provides a more effective way to adjust the im-
-pact of each keypoint along different directions. Therefore,
-our method achieves the best results on all testing scenes.
-5.3. Sensitivity to the Quality of Height Map
-As demonstrated in the above experiment, it is impor-
-tant to the iterative information fusion for achieving a more
-global understanding of 3D scene to perfect the height map
-estimation. During the iterative procedure, the problematic
-height values may be memorized to make a negative im-
-pact on the production of height map at future moment. In
-this experiment, we investigate the sensitivity of different
-approaches to the quality of height maps, by controlling
-the percentage of height values that are dissimilar to the
-ground-truth height maps. Again, we produce dissimilar
-height maps by using disturbance manipulations to change
-the object boundaries.
-At each moment, the disturbed height map is input to
-the trained model to compute the new height map, which is
-compared to the ground-truth height map for calculating the
-average L1error. In Figure 6, we compare the average L1
-errors produced by 4 different information fusion strategies
-(i.e., see the entries “w/ fusion”, “w/ fusion and memory”,
-“w/ fusion, memory and exchange” and “full strategies” in
-Table 2), which learn geometric information from height
-maps. As we can see, heavier disturbances generally lead to
-the degradation of all strategies.w/ fusion w/ fusion and memory w/ fusion, memory and exchange full strategy20%Error (m)
-  Dissimilarity to GT   Dissimilarity to GT   Dissimilarity to GT40% 60% 80% 20%271217
-40% 60% 80% 20%24610
-8
-04
-2610
-8
-40% 60% 80%Shenzhen San Francisco ChicagoFigure 6: We disturb the 2.5D height maps, which are used to examine the robustness of different information fusion ap-
-proaches. We evaluate different approaches on the testing sets of San Francisco, Shenzhen and Chicago. All results are
-reported in terms of L1errors.
-Figure 7: The five indoor training scenes selected from the S3DIS dataset [1].
-Figure 8: The successful navigation trajectories produced by VGF-Net in a complicate indoor testing scene from the S3DIS
-dataset [1].
-The strategy “w/ fusion and memory” performs the worst
-among all approaches, showing very high sensitivity to the
-quality of height maps. This result further evidences our
-finding in Sec. 5.2, where we have shown the unreliabil-
-ity of the method with memory of height information but
-without information exchange. Compared to other meth-
-ods, our full strategy yields better results. Especially, givena very high percentage (80%) of incorrect height values, our
-full strategy outperforms other methods by remarkable mar-
-gins. These results clearly demonstrate the robustness of
-our strategy.Table 3: We compare VGF-Net with/without using depth to other methods. All methods are evaluated on the outdoor sets
-(i.e., San Francisco, Shenzhen and Chicago) and the indoor set (i.e., S3DIS). Results are reported in terms of the success
-rates of navigation.
-outdoor testw/ depth w/o depth
-ground-truth depth estimated depth [3] VGF-Net
-San Francisco 100% 27% 85%
-Shenzhen 100% 34% 83%
-Chicago 100% 19% 82%
-indoor testw/ depth w/o depth
-LSTM [10] CMP [10] VGF-Net LSTM [10] CMP [10] VGF-Net
-S3DIS 71.8% 78.3% 92% 53% 62.5% 76%
-5.4. Comparison on the Navigation Task
-The quality of 2.5D height maps, which are estimated
-by the height mapping, largely determines the accuracy of
-drone navigation. In this experiment, we compare our VGF-
-Net to different mapping approaches. All methods are di-
-vided into two groups. In the first group, the approaches
-apply depth information for height mapping. Note that the
-depth information can be achieved by scanner [10], or esti-
-mated by deep network based on the RGB images [3]. The
-second group consists of approaches that only use RGB im-
-ages to reconstruct the height map. In addition to an ini-
-tial height map that can be easily obtained from various re-
-sources, our VGF-Net only requires image inputs, but can
-also accept depth information if available without changing
-any scheme architecture. We set the height of flight to be
-1030mfor drone, evaluating the success rate of 3D navi-
-gation on our outdoor dataset. Overheight (e.g., 100 m) al-
-ways leads to successful navigation, making the evaluation
-meaningless. On the indoor dataset [1] (see also Figure 7
-and Figure 8) , we report the success rate of 2D drone navi-
-gation, by fixing the height of flight to 0.5 m. All results can
-be found in Table 3.
-Obviously, using accurate depth information can yield a
-perfect success rate of navigation (see the entry “ground-
-truth depth”). Here, the depth data is directly computed
-from the synthesized 3D urban scenes, without involving
-any noise. However, due to the limitation of hardware de-
-vice, it is difficult for the scanner to really capture the accu-
-rate depth data of outdoor scenes. A simple alternative is to
-use deep network to estimate the depth based on the RGB
-image (see the entry “estimated depth”). Depth estimation
-often produces erroneous depth values for the height map-
-ping, even with the most advanced method [3], thus severely
-Input Predicted GTFigure 9: Examples of height mapping. All the height maps
-are selected from the outdoor dataset. Here, we compare
-the height maps with noise (in the first column), predicted
-height maps (in the second column) and ground-truth height
-maps (in the last column).misleading the navigation process. Similar to depth infor-
-mation, the sparse 3D keypoints used in our approach also
-provide valuable geometry information of objects. More
-importantly, our VGF-Net uses visual cues to assist the
-learning of geometric representations. Therefore, our ap-
-proach without using depth produces better results than that
-of using depth estimated by state-of-the-art techniques. We
-have shown an example of trajectory for 3D drone naviga-
-tion in Figure 1. We also show examples of height mapping
-in Figure 9, where the height map with redundant boundary
-(see the first two rows of Figure 9) or missing boundary (see
-the last two rows of Figure 9) is input to the VGF-Net. Even
-given the input height maps with much noise, our network
-still precisely recovers the height information.
-Depth data of indoor scenes (see Figure 7) can be more
-easily achieved. With the available depth information, we
-can trivially input the RGB image along with the associated
-depth to the VGF-Net, producing the height map. We com-
-pare VGF-Net to the recent approach [10] (see the entries
-“LSTM ” and “CMP”) that produces state-of-the-art indoor
-navigation accuracies. Our method achieves a better result
-under the same condition of training and testing. Without
-depth, our approach still leads to the best result among all
-image based methods. It demonstrates the generality and
-ability of our approach, in terms of stably learning useful
-information from different data sources. In Figure 8, we
-show more navigation trajectories planned by our approach
-in an indoor testing scene.
-6. Conclusions and Future Work
-The latest progress on drone navigation is largely driven
-by the active sensing and selecting the useful visual and ge-
-ometric information of surrounding 3D scenes. In this pa-
-per, we have presented VGF-Net, where we fuse visual and
-geometric information for simultaneous drone navigation
-and height mapping. Our network distills the fused infor-
-mation, which is learned from the RGB image sequences
-and an initial rough height map, constructing a novel VG
-representation to better capture object/scene relation infor-
-mation. Based on the VG representation, we propose DAM
-to establish information exchange among objects and select
-essential object relationship in a data-driven fashion. By us-
-ing residual update strategy, DAM progressively refines the
-object boundaries in the 2.5D height map and the extracted
-3D keypoints, showing its generality to various complicate
-outdoor/indoor scenes. The mapping module runs at nearly
-0.2sec on a mobile GPU, which could be further optimized
-by compression and pruning in an embedded system.
-VGF-Net eventually outputs the residual update map and
-spatial offsets, which are used for explicitly updating the
-geometric information of objects (i.e., the 2.5D height map
-and 3D keypoints). It should be noted that we currently use
-convolutional layers to learn implicit representation fromthe fused information, and update the visual representa-
-tion. The visual content of the sequence of RGB image
-shows complex patterns, which together form the global ob-
-ject/scene relationship. However, these patterns may be ne-
-glected by the implicit representation during the learning
-process. Thus, in the near future, we would like to investi-
-gate a more controllable way to update the visual represen-
-tation. Additionally, complex occlusion relations in the real
-scenarios often lead to inaccurate height mappings in the oc-
-cluded areas. In the future, we would like to further utilize
-the uncertainty map of the environment, together with the
-multi-view information to improve both the accuracy and
-the efficiency of the mapping process. Moreover, since the
-geometric modeling (triangulation of sparse keypoints) is
-commonly involved in the optimization pipeline of SLAM,
-effectively collaborating the 3D keypoints detection and the
-height mapping would be quite interesting to explore.
-Acknowledgment
-We would like to thank the anonymous reviewers for
-their constructive comments. This work was supported
-in parts by NSFC Key Project (U2001206), Guangdong
-Outstanding Talent Program (2019JC05X328), Guangdong
-Science and Technology Program (2020A0505100064,
-2018A030310441, 2015A030312015), DEGP Key Project
-(2018KZDXM058), Shenzhen Science and Technology
-Program (RCJC20200714114435012), and Guangdong
-Laboratory of Artificial Intelligence and Digital Economy
-(Shenzhen University).
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-3364, 2017. 2

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-Visualizing Adapted Knowledge in Domain Transfer
-Yunzhong Hou Liang Zheng
-Australian National University
-ffirstname.lastname [email protected]
-Abstract
-A source model trained on source data and a target
-model learned through unsupervised domain adaptation
-(UDA) usually encode different knowledge. To understand
-the adaptation process, we portray their knowledge dif-
-ference with image translation. Specifically, we feed a
-translated image and its original version to the two mod-
-els respectively, formulating two branches. Through up-
-dating the translated image, we force similar outputs from
-the two branches. When such requirements are met, dif-
-ferences between the two images can compensate for and
-hence represent the knowledge difference between models.
-To enforce similar outputs from the two branches and de-
-pict the adapted knowledge, we propose a source-free im-
-age translation method that generates source-style images
-using only target images and the two models. We visual-
-ize the adapted knowledge on several datasets with differ-
-ent UDA methods and find that generated images success-
-fully capture the style difference between the two domains.
-For application, we show that generated images enable fur-
-ther tuning of the target model without accessing source
-data. Code available at https://github.com/hou-
-yz/DA_visualization .
-1. Introduction
-Domain transfer or domain adaptation aims to bridge
-the distribution gap between source and target domains.
-Many existing works study the unsupervised domain adap-
-tation (UDA) problem, where the target domain is unla-
-beled [27, 6, 46, 1, 11]. In this process, we are interested
-in what knowledge neural networks learn and adapt.
-Essentially, we should visualize the knowledge differ-
-ence between models: a source model trained on the source
-domain, and a target model learned through UDA for the
-target domain. We aim to portray the knowledge difference
-with image generation. Given a translated image and its
-original version, we feed the two images to the source and
-the target model, respectively. It is desired that differences
-between image pairs can compensate for the knowledge dif-
-(a) Target images (real-world)
-(b) Generated source-style images
-(c) Unseen source images (synthetic)
-Figure 1: Visualization of adapted knowledge in unsuper-
-vised domain adaptation (UDA) on the VisDA dataset [38].
-To depict the knowledge difference, in our source-free im-
-age translation (SFIT) approach, we generate source-style
-images (b) from target images (a). Instead of accessing
-source images (c), the training process is guided entirely
-by the source and target models, so as to faithfully portray
-the knowledge difference between them.
-ference between models, leading to similar outputs from
-the two branches (two images fed to two different models).
-Achieving this, we could also say that the image pair repre-
-sent the knowledge difference.
-This visualization problem is very challenging and
-heretofore yet to be studied in the literature. It focuses on a
-relatively understudied field in transfer learning, where we
-distill knowledge differences from models and embed it in
-generated images . A related line of works, traditional image
-translation, generates images in the desired style utilizing
-content images and style images [7, 13, 48], and is applied
-1arXiv:2104.10602v2  [cs.CV]  1 May 2021in pixel-level alignment methods for UDA [26, 2, 44, 11].
-However, relying on images from both domains to indicate
-the style difference, such works cannot faithfully portray
-the knowledge difference between source and target models ,
-and are unable to help us understand the adaptation process.
-In this paper, we propose a source-free image translation
-(SFIT) approach, where we translate target images to the
-source style without using source images. The exclusion of
-source images prevents the system from relying on image
-pairs for style difference indication, and ensures that the
-system only learns from the two models . Specifically, we
-feed translated source-style images to the source model and
-original target images to the target model, and force similar
-outputs from these two branches by updating the generator
-network. To this end, we use the traditional knowledge dis-
-tillation loss and a novel relationship preserving loss, which
-maintains relative channel-wise relationships between fea-
-ture maps. We show that the proposed relationship preserv-
-ing loss also helps to bridge the domain gap while chang-
-ing the image style, further explaining the proposed method
-from a domain adaptation point of view. Some results of
-our method are shown in Fig. 1. We observe that even un-
-der the source-free setting, knowledge from the two models
-can still power the style transfer from the target style to the
-source style (SFIT decreases color saturation and whitens
-background to mimic the unseen source style).
-On several benchmarks [19, 36, 39, 38], we show that
-generated images from the proposed SFIT approach signifi-
-cantly decrease the performance gap between the two mod-
-els, suggesting a successful distillation of adapted knowl-
-edge. Moreover, we find SFIT transfers the image style
-at varying degrees, when we use different UDA methods
-on the same dataset. This further verifies that the SFIT
-visualizations are faithful to the models and that different
-UDA methods can address varying degrees of style differ-
-ences. For applications, we show that generated images can
-serve as an additional cue and enable further tuning of target
-models. This also falls into a demanding setting of UDA,
-source-free domain adaptation (SFDA) [17, 20, 24], where
-the system has no access to source images.
-2. Related Work
-Domain adaptation aims to reduce the domain gap be-
-tween source and target domains. Feature-level distribution
-alignment is a popular strategy [27, 6, 46, 40]. Long et
-al. [27] use the maximum mean discrepancy (MMD) loss
-for this purpose. Tzeng et al. [46] propose an adversarial
-method, ADDA, with a loss function based on the gen-
-erative adversarial network (GAN). Pixel-level alignment
-with image translation is another popular choice in UDA
-[26, 2, 44, 42, 1, 11]. Hoffman et al . propose the Cy-
-CADA [11] method based on CycleGAN [48] image trans-
-lation. Other options are also investigated. Saito et al. [40]align the task-specific decision boundaries of two classi-
-fiers. Source-free domain adaptation (SFDA) does notuse
-the source data and therefore greatly alleviates the privacy
-concerns in releasing the source dataset. As an early at-
-tempt, AdaBN [22] adapts the statistics of the batch normal-
-ization layers in the source CNN to the target domain. Li et
-al. [20] generate images with the same distribution of the
-target images and use them to fine-tune the classifier. Liang
-et al. [24] fine-tune a label smoothed [34] source model on
-the target images. To the authors’ knowledge, there is still
-yet to be any visualization that can indicate what models
-learn during adaptation.
-Knowledge distillation transfers knowledge from a pre-
-trained teacher model to a student model [10], by maxi-
-mizing the mutual information between teacher outputs and
-student outputs. Some existing works consider the relation-
-ship between instance or pixels for better distillation per-
-formance [45, 23, 37]. Instead of distilling teacher knowl-
-edge on a given training dataset, data-free knowledge dis-
-tillation (DFKD) [30, 35, 3, 33, 8, 47] first generates train-
-ing data and then learns a student network on this gener-
-ated dataset. Training data can be generated by aligning
-feature statistics [30, 8, 47], enforcing high teacher confi-
-dence [30, 35, 3, 8, 47], and adversarial generation of hard
-examples for the student [33, 47]. In [8, 47], batch normal-
-ization statistics are matched as regularization. Our work,
-while also assuming no access to source images, differs sig-
-nificantly from these works in that our image translation
-has to portray the transferred knowledge, whereas data-free
-knowledge distillation just generates whatever images that
-satisfy the teacher networks.
-Image translation renders the same content in a differ-
-ent artistic style. Some existing works adopt a GAN-based
-system for this task [26, 44, 14, 48, 11], while others use a
-pre-trained feature extractor for style transfer [7, 15, 32, 13].
-Zhuet al. adopt a cycle consistency loss in the image trans-
-lation loop to train the CycleGAN system [48]. Gatys
-et al . consider a content loss on high-level feature maps,
-and a style loss on feature map statistics for style transfer
-[7]. Huang and Belongie [13] propose a real-time AdaIN
-style transfer method by changing the statistics in instance
-normalization layers. Based on AdaIN, Karras et al. pro-
-pose StyleGAN for state-of-the-art image generation [16].
-Our work differs from traditional image translations in that
-rather than images from the two domains, only models from
-two domains are used to guide the image update.
-3. Problem Formulation
-To achieve our goal, i.e.,visualizing adapted knowledge
-in UDA, we translate a image xfrom a certain domain to
-a new imageex. It is hoped that feeding the original image
-to its corresponding model (trained for that certain domain)
-and the generated image to the other model can minimize
-2target image 𝒙𝒙 generated image �𝒙𝒙generatorsource
-CNN
-target
-CNNrelationship 
-preserving loss
-classifier classifierknowledge
-distillation lossFigure 2: The proposed source-free image translation (SFIT) method for visualizing the adapted knowledge in UDA. The
-system includes two branches: original target images are fed to the target CNN, whereas generated source-style images are
-fed to the source CNN. We minimize the knowledge distillation loss and the relationship preserving loss, and update the
-generator network accordingly. If the two branches get similar results while adopting different models, then the difference
-between the original target image xand the generated source-style image exshould be able to mitigate and therefore exhibit
-the knowledge difference between models. Dashed lines indicate fixed network parameters.
-the output difference between these two branches. The up-
-date process is directed only by the source model fS(
)and
-the target model fT(
), and we prevent access to the images
-from the other domain to avoid distractions. We formulate
-the task of visualizing adapted knowledge as a function of
-the source model, the target model, and the image from a
-certain domain,
-G(fS; fT;x)!ex: (1)
-In contrast, traditional image translation needs access to im-
-ages from both domains for content and style specification.
-In addition to the source image xSand the target image xT,
-traditional image translation also relies on certain neural
-network d(
)as the criterion. Instead of the source and tar-
-get models, ImageNet [4] pre-trained VGG [43] and adver-
-sarially trained discriminator networks are used for this task
-in style transfer [7, 13] and GAN-based methods [48, 11],
-respectively. Traditional image translation task can thus be
-formulated as,
-G(d;xS;xT)!ex: (2)
-Comparing our goal in Eq. 1 and traditional image transla-
-tion in Eq. 2, we can see a clear gap between them. Tradi-
-tional image translation learns the style difference indicated
-byimages from both domains, whereas our goal is to learn
-to visualize the knowledge difference between the source
-and target models fS(
); fT(
).
-4. Method
-To investigate what neural networks learn in do-
-main adaptation, we propose source-free image translation
-(SFIT), a novel method that generates source-style images
-from original target images, so as to mitigate and represent
-the knowledge difference between models.4.1. Overview
-Following many previous UDA works [6, 27, 46, 24], we
-assume that only the feature extractor CNN in the source
-model is adapted to the target domain. Given a source CNN
-fS(
)and a target CNN fT(
)sharing the same classifier
-p(
), we train a generator g(
)for the SFIT task. We discuss
-why we choose this translation direction in Section 4.3. As
-the training process is source-free, for simplicity, we refer
-to the target image as xinstead of xTin what follows.
-As shown in Fig. 2, given a generated image ex=g(x),
-the source model outputs a feature map fS(ex)and a prob-
-ability distribution p(fS(ex))over all Cclasses. To depict
-the adapted knowledge in the generated image, in addition
-to the traditional knowledge distillation loss, we introduce a
-novel relationship preserving loss, which maintains relative
-channel-wise relationships between the target-image-target-
-model feature map fT(x)and the generated-image-source-
-model feature map fS(ex).
-4.2. Loss Functions
-With a knowledge distillation loss LKDand a relationship
-preserving lossLRP, we have the overall loss function,
-L=LKD+LRP: (3)
-In the following sections, we detail the loss terms.
-Knowledge distillation loss. In the proposed source-
-free image translation method, portraying the adapted
-knowledge in the target model fT(
)with source model
-and generator combined fS(g(
))can be regarded as a spe-
-cial case of knowledge distillation, where we aim to distill
-the adapted knowledge to the generator. In this case, we
-include a knowledge distillation loss between generated-
-image-source-model output p(fS(ex))and target-image-
-3target-model output p(fT(x)),
-LKD=DKL(p(fT(x)); p(fS(ex))); (4)
-whereDKL(
;
)denotes the Kullback-Leibler divergence.
-Relationship preserving loss. Similar classification
-outputs indicate a successful depiction of the target model
-knowledge on the generated images. As we assume a fixed
-classifier for UDA, the global feature vectors from the tar-
-get image target CNN and the generated image source CNN
-should be similar after a successful knowledge distillation.
-Promoting similar channel-wise relationships between fea-
-ture maps fT(x)andfS(ex)helps to achieve this goal.
-Previous knowledge distillation works preserve relative
-batch-wise or pixel-wise relationships [45, 23]. However,
-they are not suitable here for the following reasons. Relative
-batch-wise relationships can not effectively supervise the
-per-image generation task. Besides, the efficacy of pixel-
-wise relationship preservation can be overshadowed by the
-global pooling before the classifier. By contrast, channel-
-wise relationships are computed on a per-image basis, and
-are effective even after global pooling. As such, we choose
-the channel-wise relationship preserving loss that is com-
-puted in the following manner.
-Given feature maps fT(x); fS(ex), we first reshape them
-into feature vectors FSandFT,
-fS(ex)2RDHW!F S2RDHW;
-fT(x)2RDHW!F T2RDHW;(5)
-where D; H , and Ware the feature map depth (number of
-channels), height, and width, respectively. Next, we calcu-
-late their channel-wise self correlations, or Gram matrices,
-GS=FS
FT
-S; G T=FT
FT
-T; (6)
-where GS; GT2RDD. Like other similarity preserving
-losses for knowledge distillation [45, 23], we then apply the
-row-wiseL2normalization,
-eGS[i;:]=GS[i;:]
-2;eGT[i;:]=GT[i;:]
-2; (7)
-where [i;:]indicates the i-th row in a matrix. At last, we
-define the relationship preserving loss as mean square error
-(MSE) between the normalized Gram matrices,
-LRP=1
-D
-F; (8)
-wherek
kFdenotes the Frobenius norm (entry-wise L2
-norm for matrix). In Section 4.3, we further discuss the rela-
-tionship preserving loss from the viewpoint of style transfer
-and domain adaptation, and show it can align feature map
-distributions in a similar way as style loss [7] for style trans-
-fer and MMD loss [27] for UDA, forcing the generator to
-portray the knowledge difference between the two models.
-(a) Relationship preserving loss
- (b) Traditional style loss
-Figure 3: Comparison between the proposed relationship
-preserving loss and the traditional style loss. In (a) and (b),
-given 256-dimensional feature maps, we show differences
-of row-wise normalized Gram matrix (Eq. 8) and original
-Gram matrix (Eq. 9). Deeper colors indicate larger dif-
-ferences and therefore stronger supervision. The proposed
-relationship preserving loss provides evenly distributed su-
-pervision for all channels, whereas the traditional style loss
-focuses primarily on several channels.
-4.3. Discussions
-Why transfer target images to the source style. Ac-
-cording to the problem formulation in Eq. 1, we should be
-able to visualize the adapted knowledge by generating ei-
-ther source-style images from target images, or target-style
-images from source images. In this paper, we select the for-
-mer direction as it might be further applied in fine-tuning
-the target model (see Section 5.4 for application).
-Style transfer with the relationship preserving loss.
-The proposed relationship preserving loss can be regarded
-as a normalized version of the traditional style loss intro-
-duced by Gatys et al. [7],
-Lstyle=1
-D2kGSGTk2
-F; (9)
-which computes MSE between Gram matrices.
-In the proposed relationship preserving loss (Eq. 8), in-
-stead of original Gram matrices, we use a row-wise normal-
-ized version. It focuses on relative relationships between
-channels, rather than absolute values of self correlations as
-in the traditional style loss. Preserving relative relation-
-ships provides more evenly-distributed supervision for all
-channels, instead of prioritizing several channels as in the
-traditional style loss (Fig. 3). Experiments find this evenly-
-distributed supervision better preserves the foreground ob-
-ject and allows for easier training and higher performance,
-while also changing the image style (see Section 5.5).
-Distribution alignment with the relationship preserv-
-ing loss. As proved by Li et al. [21], the traditional style
-4lossLstyleis equivalent to the MMD loss [27] for UDA. We
-can also see the relationship preserving loss as a modified
-version of the MMD loss, which aligns the distribution of
-the generated image source CNN feature map fS(ex)to the
-target image target CNN feature map fT(x).
-5. Experiments
-5.1. Datasets
-We visualize the knowledge difference between source
-and target models on the following datasets.
-Digits is a standard UDA benchmark that focuses on
-10-class digit recognition. Specifically, we experiment on
-MNIST [19], USPS, and SVHN [36] datasets.
-Office-31 [39] is a standard benchmark for UDA that
-contains 31 classes from three distinct domains: Amazon
-(A), Webcam (W), and DSLR (D).
-VisDA [38] is a challenging large-scale UDA benchmark
-for domain adaptation from 12 classes of synthetic CAD
-model images to real-world images in COCO [25].
-5.2. Implementation Details
-Source and target models. We adopt source and tar-
-get models from a recent SFDA work SHOT-IM [24] if not
-specified. SFDA is a special case of UDA, and it is even
-more interesting to see what machines learn in the absence
-of source data. We also include UDA methods DAN [27]
-and ADDA [46] for SFIT result comparisons. For network
-architectures, on digits dataset, following Long et al. [28],
-we choose a LeNet [18] classifier. On Office-31 and VisDA,
-we choose ResNet-50 and ResNet-101 [9], respectively.
-Generator for SFIT. We use a modified CycleGAN [48]
-architecture with 3 residue blocks due to memory concerns.
-Training schemes. During training, we first initialize
-the generator as a transparent filter, which generates im-
-ages same as the original input. To this end, we use the
-ID lossLID=kexxk1and the content loss Lcontent =
-kfS(ex)fS(x)k2to train the generator for initialization.
-The initialization performance is shown in Table 4, where
-we can see a mild 1.9% accuracy drop from original tar-
-get images. Then, we train the generator with the overall
-loss function in Eq. 3 for visualizing the adapted knowl-
-edge. Specifically, we use an Adam optimizer with a cosine
-decaying [31] learning rate starting from 3104and a
-batch size of 16. All experiments are finished using one
-RTX-2080Ti GPU.
-5.3. Evaluation
-Recognition accuracy on generated images. To ex-
-amine whether the proposed SFIT method can depict the
-knowledge difference, in Table 1-3, we report recogni-
-tion results using the generated-image-source-model branch
-(referred as “generated images”). On the digits dataset,Method SVHN!MNIST USPS!MNIST MNIST!USPS
-Source only [11] 67.10.6 69.63.8 82.20.8
-DAN [27] 71.1 - 81.1
-DANN [6] 73.8 73 85.1
-CDAN+E [28] 89.2 98.0 95.6
-CyCADA [11] 90.40.4 96.50.1 95.60.4
-MCD [40] 96.20.4 94.10.3 94.20.7
-GTA [41] 92.40.9 90.81.3 95.30.7
-3C-GAN [20] 99.40.1 99.30.1 97.30.2
-Source model [24] 72.30.5 90.51.6 72.72.3
-Target model [24] 98.80.1 98.10.5 97.90.2
-Generated images 98.60.1 97.40.3 97.60.3
-Table 1: Classification accuracy (%) on digits datasets. In
-Table 1-3, “Generated images” refers to feeding images
-generated by SFIT to the source model.
-Method A!W D!W W!D A!D D!A W!A Avg.
-ResNet-50 [9] 68.4 96.7 99.3 68.9 62.5 60.7 76.1
-DAN [27] 80.5 97.1 99.6 78.6 63.6 62.8 80.4
-DANN [6] 82.6 96.9 99.3 81.5 68.4 67.5 82.7
-ADDA [46] 86.2 96.2 98.4 77.8 69.5 68.9 82.9
-JAN [29] 86.0 96.7 99.7 85.1 69.2 70.7 84.6
-CDAN+E [28] 94.1 98.6 100.0 92.9 71.0 69.3 87.7
-GTA [41] 89.5 97.9 99.8 87.7 72.8 71.4 86.5
-3C-GAN [20] 93.7 98.5 99.8 92.7 75.3 77.8 89.6
-Source model [24] 76.9 95.6 98.5 80.3 60.6 63.4 79.2
-Target model [24] 90.8 98.4 99.9 88.8 73.6 71.7 87.2
-Generated images 89.1 98.1 99.9 87.3 69.8 68.7 85.5
-Fine-tuning 91.8 98.7 99.9 89.9 73.9 72.0 87.7
-Table 2: Classification accuracy (%) on the Office-31
-dataset. In Table 2 and Table 3, “Fine-tuning” refers to tar-
-get model fine-tuning result with both generated images and
-target images (see Section 5.4 for more details).
-Method plane bcycl bus car horse knife mcycl person plant sktbrd train truck per-class
-ResNet-101 [9] 55.1 53.3 61.9 59.1 80.6 17.9 79.7 31.2 81.0 26.5 73.5 8.5 52.4
-DAN [27] 87.1 63.0 76.5 42.0 90.3 42.9 85.9 53.1 49.7 36.3 85.8 20.7 61.1
-DANN [6] 81.9 77.7 82.8 44.3 81.2 29.5 65.1 28.6 51.9 54.6 82.8 7.8 57.4
-JAN [29] 75.7 18.7 82.3 86.3 70.2 56.9 80.5 53.8 92.5 32.2 84.5 54.5 65.7
-ADDA [46] 88.8 65.7 85.6 53.1 74.9 96.2 83.3 70.7 75.9 26.4 83.9 32.4 69.7
-MCD [40] 87.0 60.9 83.7 64.0 88.9 79.6 84.7 76.9 88.6 40.3 83.0 25.8 71.9
-CDAN+E [28] 85.2 66.9 83.0 50.8 84.2 74.9 88.1 74.5 83.4 76.0 81.9 38.0 73.9
-SE [5] 95.9 87.4 85.2 58.6 96.2 95.7 90.6 80.0 94.8 90.8 88.4 47.9 84.3
-3C-GAN [20] 94.8 73.4 68.8 74.8 93.1 95.4 88.6 84.7 89.1 84.7 83.5 48.1 81.6
-Source model [24] 58.3 17.6 54.2 69.9 64.4 5.5 82.2 30.7 62.2 24.6 86.2 6.0 46.8
-Target model [24] 92.5 84.7 81.3 54.6 90.5 94.7 80.9 79.1 90.8 81.5 87.9 50.1 80.7
-Generated images 88.9 65.8 83.0 61.7 88.5 76.8 89.5 69.6 91.4 51.9 84.3 34.3 73.8
-Fine-tuning 94.3 79.0 84.9 63.6 92.6 92.0 88.4 79.1 92.2 79.8 87.6 43.0 81.4
-Table 3: Classification accuracy (%) on the VisDA dataset.
-in terms of performance gaps, the knowledge differ-
-ences between source and target models are 26.5% on
-SVHN!MNIST, 7.6% on USPS !MNIST, and 25.2%
-on MNIST!USPS. Generated images from SFIT bridges
-these differences to 0.2%, 0.7%, and 0.3%, respectively.
-On the Office-31 dataset, the performance gap between the
-two models is 8.0% on average, and the generated images
-shrink this down to 1.7%. Notably, the performance drops
-from the target-image-target-model branch to the generated-
-image-source-model branch are especially pronounced on
-D!A and W!A, two settings that transfer Amazon im-
-ages with white or no background to real-world background
-5(a) Target images (MNIST)
-(b) Generated source-style images
-(c) Unseen source images (SVHN)
-Figure 4: Results from the SFIT method on digits datasets
-SVHN!MNIST. In Fig. 1 and Fig. 4-6, we show in (a):
-target images, (b): generated source-style images, each of
-which corresponds to the target image above it, and (c):
-the unseen source images. For gray-scale target images
-from MNIST, our SFIT approach adds random RGB colors
-to mimic the full-color style in the unseen source (SVHN)
-without changing the content.
-(a) Target images (Webcam)
-(b) Generated source-style images
-(c) Unseen source images (Amazon)
-Figure 5: Results from the SFIT method on the Office-
-31 dataset Amazon !Webcam. Our translation method
-whitens backgrounds while increasing contrast ratios of the
-object (Webcam) for more appealing appearances as in the
-online shopping images (Amazon).
-in Webcam or DSLR. In fact, in experiments we find gen-
-erating an overall consistent colored background is very de-
-manding, and the system usually generates a colored back-
-ground around the outline of the object. On the VisDAdataset, generated images bridge the performance gap from
-33.9% to 6.9%, even under a more demanding setting and
-a larger domain gap going from real-world images to syn-
-thetic CAD model images. Overall, on all three datasets,
-generated images significantly mitigate the knowledge dif-
-ference in terms of performance gaps, indicating that the
-proposed SFIT method can successfully distill the adapted
-knowledge from the target model to the generated images.
-Visualization of source-free image translation results.
-For digits datasets SVHN !MNIST (Fig. 4), the generator
-learns to add RGB colors to the gray-scale MNIST (target)
-images, which mimics the full-color SVHN (source) im-
-ages. For Office-31 dataset Amazon !Webcam (Fig. 5), the
-generated images whiten the background, while having a
-white or no background rather than real-world background
-is one of the main characteristics of the Amazon (source)
-domain when compared to Webcam (target). Moreover,
-Amazon online shopping images also have higher contrast
-ratios for more appealing appearances, and our translated
-images also capture these characteristics, e.g., keys in the
-calculator, case of the desktop computer. For VisDA dataset
-SYN!REAL (Fig. 1 and Fig. 6), the generator learns to
-decrease the overall saturation of the real-world (target)
-objects which makes them more similar to the synthetic
-(source) scenario, while at the same time whitens the back-
-ground, e.g., horse, truck, and plane in Fig. 1, car and skate-
-board in Fig. 6, and brings out the green color in the plants.
-Overall, image generation results exhibit minimal content
-changes from target images, while successfully capturing
-theunseen source style.
-In terms of visual quality, it is noteworthy that generation
-results for digits datasets SVHN !MNIST contain colors
-and patterns that are not from the source domain, whereas
-our results on the Office-31 dataset and VisDA dataset are
-more consistent with the unseen source. Due to the lack
-of source images, rather than traditional image translation
-approaches [7, 13, 11, 44], SFIT only relies on source and
-target models, and portrays adapted knowledge according
-to the two models. Since a weaker LeNet classifier is used
-for the digits dataset, it is easier to generate images that sat-
-isfy the proposed loss terms without requiring the generated
-images to perfectly mimic the source style. On Office-31
-and VisDA datasets, given stronger models like ResNet, it
-is harder to generate images that can satisfy the loss terms.
-Stricter restrictions and longer training time lead to gener-
-ation results more coherent with unseen source images that
-also have better visual quality.
-Visualization for different UDA methods. In Fig. 7,
-we show SFIT visualization results using different UDA
-methods. Given source and target domain, a traditional im-
-age translation method generates a certain type of images
-regardless of the UDA methods, indicating its incapabil-
-ity of presenting the knowledge difference between mod-
-6(a) Target images (real-world)
-(b) Generated source-style images
-(c) Unseen source images (synthetic)
-Figure 6: Results from the SFIT method on the VisDA dataset SYN !REAL. Our translation method decreases the target
-(real-world) image saturation and whitens the background while keeping the semantics unchanged.
-(a)
- (b)
- (c)
- (d)
-Figure 7: SFIT results on VisDA dataset with different UDA
-methods. (a) Target images; (b) DAN [27]; (c) ADDA [46];
-(d) SHOT-IM [24].
-els. In contrast, the proposed SFIT method generates differ-
-ent images for different UDA methods. Specifically, when
-comparing visualization results of the adapted knowledge
-in DAN [27], ADDA [46], and SHOT-IM [24], we find
-stronger UDA methods can better transfer the target style
-to the unseen source style. As shown in Fig. 7, in terms
-of whitening the background for style transfer, SFIT re-sults on ADDA are less coherent than SHOT-IM but better
-than DAN. This further verifies that our SFIT method in-
-deed visualizes the knowledge difference between models,
-and stronger adaptation methods can better endure the style
-difference (leading to larger knowledge difference and thus
-stronger style transfer results).
-5.4. Application
-The generated images from SFIT allows for further tun-
-ing of the target model in SFDA systems, where no source
-image is available. We include a diversity loss on all train-
-ing samples to promote even class-wise distributions,
-Ldiv=H
-ExPtarget(x)[p(fT(x))]

-; (10)
-whereH(
)denotes the information entropy function. We
-also incluse a pseudo-label fine-tuning loss, if pseudo
-label ^yS= arg max p(fS(ex))from the generated-
-image-source-model branch equals to the pseudo label
-^yT= arg max p(fT(x))from the target-image-target-
-model branch. We then use this pseudo label ^y= ^yS= ^yT
-to fine-tune the target model,
-Lpseudo =(
-H(p(fT(x));^y); if^y= ^yS= ^yT;
-0; else;(11)
-whereH(
;
)denotes the cross entropy function. We com-
-bine these two loss terms in Eq. 10 and Eq. 11 to give an
-overall fine-tuning loss LFT=Ldiv+Lpseudo .
-7(a)
- (b)
- (c)
- (d)
-Figure 8: Visualization results on VisDA dataset with dif-
-ferent distribution alignment methods. (a) Target images;
-(b) BN stats alignment [12]; (c) traditional style loss [7];
-(d) relationship preserving loss.
-As an additional cue, supervision from generated-image-
-source-model further boosts target model SFDA perfor-
-mance. On Office-31, fine-tuning brings a performance
-improvement of 0.4% according to Table 2. On VisDA,
-fine-tuning improves the target model accuracy by 0.7% as
-shown in Table 3. These improvements are statistically very
-significant ( i.e.,p-value <0.001 over 5 runs), and introduce
-a real-world application for images generated by SFIT.
-5.5. Comparison and Variant Study
-Comparison with the BatchNorm statistics alignment
-method [12]. Hou et al. propose to match the batch-wise
-feature map statistics so as to directly generate images that
-mimic the source style. Specifically, they explore the Batch-
-Norm (BN) statistics stored in the BN layers in the source
-model for style indication, and match them against that of
-the generated images. Using their approach, we can mildly
-change the image to the unseen source style (see Fig. 8) and
-slightly reduce the performance difference between the two
-branches (see Table 4). With that said, their lack of output
-alignments between the two branches (only supervisions
-from the source branch ) results in much lower quantita-
-tive performance and under-performing style transfer qual-
-ity when compared to the proposed method.
-Effect of the knowledge distillation loss. The knowl-
-edge distillation loss transfers the adapted knowledge to the
-generated images, and the removal of it results in a 1.1%
-performance drop.
-Effect of the relationship preserving loss. As shown in
-Fig. 8, the traditional style loss can successfully transfer the
-target image to the source style on its own. However, usingVariantLKDLRP accuracy (%)
-Target image - - 46.8
-Initialized g(
) 44.9
-BN stats alignment [12] 51.7
-w/oLKD 3 72.7
-w/oLRP 3 71.2
-LRP!L style 3Lstyle[7] 66.4
-LRP!L batch 3Lbatch[45] 71.2
-LRP!L pixel 3Lpixel[23] 70.9
-SFIT 3 3 73.8
-Table 4: Variant study on VisDA dataset. “Initialized g(
)”
-refers to our transparent filter initialization in Section 5.2.
-it causes a 4.8% performance drop compared to the “w/o
-LRP” variant (see Table 4), suggesting it being unsuitable
-for SFIT. On the other hand, the batch-wise or pixel-wise
-relationship preserving variants [45, 23] are found not use-
-ful, as they fail to improve over the “w/o LRP” variant.
-In contrast, the proposed channel-wise relationship pre-
-serving lossLRPcan effectively improve the recognition ac-
-curacy on the generated images, as the inclusion of it leads
-to a 2.6% performance increase. Moreover, as shown in
-Fig. 8, similar to the traditional style loss, using only the re-
-lationship preserving loss can also effectively transfer the
-target image to the unseen source style. Besides, focus-
-ing on the relative channel-wise relationship instead of the
-absolute correlation values, the proposed relationship pre-
-serving loss can better maintain the foreground object (less
-blurry and more prominent) while transferring the overall
-image style, leading to higher recognition accuracy.
-6. Conclusion
-In this paper, we study the scientific problem of visu-
-alizing the adapted knowledge in UDA. Specifically, we
-propose a source-free image translation (SFIT) approach,
-which generates source-style images from original target
-images under the guidance of source and target models.
-Translated images on the source model achieve similar re-
-sults as target images on the target model, indicating a suc-
-cessful depiction of the adapted knowledge. Such images
-also exhibit the source style, and the extent of style trans-
-fer follows the performance of UDA methods, which fur-
-ther verifies that stronger UDA methods can better address
-the distribution difference between domains. We show that
-the generated images can be applied to fine-tune the target
-model, and might help other tasks like incremental learning.
-Acknowledgement
-This work was supported by the ARC Discovery Early
-Career Researcher Award (DE200101283) and the ARC
-Discovery Project (DP210102801).
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txt/2105.11780.txt

@@ -1,644 +0,0 @@
-Look Inside. Predicting Stock Prices by Analysing an Enterprise 
-Intranet Social Network and Using Word Co -Occurrence 
-Networks  
- 
-Fronzetti Colladon, A., & Scettri, G.  
- 
- 
-This is the accepted manuscript after the review process, but prior to final layout 
-and copyediting . Please cit e as:  
- 
-Fronzetti Colladon, A., & Scettri, G. (2019). Look Inside. Predicting Stock 
-Prices by Analysing an Enterprise Intranet Social Network and Using Word Co -
-Occurrence  Networks. International Journa l of Entrepreneurship and Small 
-Business, 36(4), 378 -391. https://dx.doi .org/10.1504/IJESB.2019.098986  
- 
-This work is licensed under the Creative Commons Attribution -
-NonCommercial -NoDerivatives 4.0 International License. To view a copy of 
-this license, visit http://crea tivecommons.org/licenses/by -nc-nd/4.0/  or send a 
-letter to Creative Commons, PO Box 1866, Mountain View, CA 94042, USA.  1 
- Look Inside. Predicting Stock Prices by Analysing an 
-Enterprise Intranet Social Network and Using Word Co -
-Occurrence Networks  
- 
-Fronzetti Colladon , A., & Scettri, G. 
- 
- 
- 
- 
-Abstract  
-This study looks into the employees’  communication behaviours taking place in an intranet  social 
-network , offering novel metrics of Semantic and Social Network Analysis, which can help predict a 
-company stock price.  To this purpose, w e studied the intranet forum  of a large Italian company, 
-exploring  the interaction s and the use of language of about 8,000 employees . We analyse d more 
-than 48,000 news and comments , over a period of 94 weeks . In addition to using more traditional 
-semantic and social network metrics,  we built a network  linking words  included in  the general 
-discourse. In this network, we focused on the posit ion of the node representing  the company brand.  
-We f ound that a lower se ntiment  of the language used , a higher  betwe enness centrality of the 
-company brand, a denser word co -occurrence network and more equally distributed centrality 
-scores  of employees  (lower group betweenness centrality) are all significant predictors of higher  
-stock  prices.  Our findings contribute to the strea m of research concerned with  the prediction of 
-stock prices , offering new metrics that can be helpful for scholars , company man agers  and 
-professional investors  and could be integrated in to existing forecasting models  to improve their 
-accuracy . We also show the importance of looking at internal communication streams  while  
-analysing  a company’s financial performance . Lastly , we contribute to the research on word co -
-occurrence network s by extend ing their field of application.  
- 2 
- Keywords:  stock price; economic forecasting; intranet; social network;  web forum;  semantic 
-analysis; word co -occurrence  network . 
- 
-1. Introduction  
-The question  whether  stock market prices are predictable is frequent in literature, especially after 
-financial crisis  (Cowles, 1933; Bollerslev and Ole Mikkelsen, 1996; Barro, 2015) . In recent years , 
-new approaches were presented, comprising the analysis of co mplex system s and data mining and 
-machine learning techniques (Kuo, Chen and Hwang, 2001; Sornette, 2003) . With the complex 
-system approach it is poss ible to model  the influence  of the  various agent s that operate in a market 
-(investors, companies , bank s, nations , etc.. ), investigating  their reciprocal interactions (Grimm, 
-2005) . Data mining  offers  the possibility to analyse  large volumes of data , retrieved  from  different 
-sources , such as  stock indices , social media  or other online platforms . Nowadays researchers  can 
-take advantage of  datasets never seen before, in which interesting information can be hidden.  
-In this paper, we combine methods and tools from Social Network  and Semantic Analysis 
-(Wasserman and Faust, 1994; Aggarwal and Zhai, 2013) , while investigating the interactions taking 
-place in the intranet  forum  of a large Italian company. We study the interaction patterns of the 
-company employees with regard to their activity levels, network positions an d use of language , 
-while posting news and comments . Additionally, we pro pose a novel method to transform the 
-general discourse into a network of words (Danowski, 2009) , thus analys ing the positions and 
-fluctuations of the company brand in that network.  Our scope is  to understand if there are  hidden  
-associations  between th ese patterns and the  company stock price  at specific time lags . In this 
-regard, we offer a novel contribut ion since  we use a new information source – which, to the extent 
-of our knowledge, has never been used for the same exact purpose – and study original metrics 
-obtained from  the combination  of semantic and social network analysis . In other words, the 
-objective of this paper is to extract new variables fr om the analysis of the communication  taking 3 
- place in a large  intranet social network , show ing the  value  of these new predictors  for the 
-forecasting of stock prices : we investigate  if it is possible to look at employees’ communication 
-behaviours to better predict the firm market value . This research also  offers an additional  
-contribut ion to increase  the knowledge about  possible use s of word co -occurrence network s.  
- 
-2. Network  and Semantic  Analysis for Stock Market Predictions  
-The use of network theory to analyse  stock market trend s evolved around two main approaches: 
-empirical and theoretical. T he first is centred  on the  experimental way s by which network s are 
-created, as in the work of  Sun et al. (2015) , who connect ed stock  prices  with other market  attributes 
-(e.g., trading volume s or net return s). The t heoretical approach , on the other hand,  is focused on the 
-evaluation  of the  problem in a general form : given some mathematical conditions related to  the 
-network, the aim is to demonstra te the existence and uniqueness of a solution (Barucca  et al. , 2016) .  
-Studies applying  network theor ies were  successfully implemented to analyse  the behaviour  of 
-Brazilian (Tabak et al. , 2009) , Chinese (Huang, Zhuang and Yao, 2009)  and Indian (Pan and Sinha, 
-2007)  stock marke ts. Tabak et al. (2009)  tried to figure out if the Brazilian stock price returns 
-present ed a power law distribution . Their result s show ed that, for most of the  study  period , a power 
-law distribution is not representative of the phenomenon.  This suggests that the dynamics of stock 
-prices may change abruptly , being more complicated than a power law distribution,  especially when 
-critical events occur . Huang and colleagues (2009)  used a threshold method to build a social 
-network considering  the correlations among  1080 stocks  in the Chinese market  and their daily 
-prices for about four years. Testing the topological stability of the network, they found it to be  
-robust against random vertex failures, but fragile against targeted  attack s; in this way they offered 
-insights for risk and portfolio management . Similarly, Pan and Sinha (2007)  analysed  the cross -
-correlation matrix of stock price s fluctuations in the Indian National Stock Exchange. This market 
-exhibit ed strong er correlations when compared to  more  mature markets , such as the New York 4 
- Stock Exchange . Pan and Sinha (2007) showed that the existence of  an internal structure made of 
-multiple  connected  groups is a  possible indicator of market development.  
-Other authors addressed the problem of interdependence between stock markets (Morana and 
-Beltratti, 2008; Sedik and Williams, 2011) , suggesting the inclusion of control  indices  while 
-making stock price predictions. In particular,  Sedik and Williams  (2011)  used a GARCH model to 
-study the influence of the volatility of U.S. and regional equity markets on the conditional volatility 
-of stock prices in  the Gulf Cooperation Council’s m arket. Morana and  Beltratti  (2008) , showed a 
-link among the variance s of the stock market indices of US, UK, Japan and Germany . 
-Recently , the improvement of sentiment analysis and other social media  related  research , offered 
-new possibilities, combining stock market predictions with  metrics extracted from online  platforms , 
-such as Twitter or Facebook  (Zhang, Fuehres and Gloor, 2011; Chen and Lazer, 2013; Makrehchi, 
-Shah and Liao, 2013) . Measuring  the sentiment  of the conversations about a specific company, 
-scholars proved the influence of positive and negative feelings on stock market prices (Chung and 
-Liu, 2011; Elshendy et al. , 2017) . To this purpose, Khadjeh et al.  (2014)  and Schumaker and Chen 
-(2009)  used sentiment analysis combined with linear regression models  and support vector 
-machines . In the  former  study, authors used the text of breaking financial new s-headlines  to 
-forecast  currency  movements in the foreign exchange market . In the latter study, autho rs succeeded 
-in partially  predict ing future stock price s twenty minutes after a financial article was released : they 
-used several different textual representations  – such as  Bag of Words, Noun Phrases  (Caropreso and 
-Matwin, 2006)  and Named Entities  (Diesner and Carley, 2005 ). Zhang et al. (2011)  analysed  
-Twitter  with a similar purpose, tagging tweets according to feelings of  fear, general concern  and 
-hope ; they  found a negative correlation between  the sentiment trend of the tagging variables and  the 
-Dow  Jones, NASDAQ and S&P 500 . Antweiler and Frank (2004)  studied the influence of messages 
-in Yahoo!Finance over a set of  45 stock market prices  of private companies . Similarly, Xie et al. 
-(2013)  crawled  Yahoo!Finance  and developed  a tree representation  for word s in sentences,  which 
-performed  significantly  better than  approaches based on  bag-of-words  in predicting  the polarity  of 5 
- stock price s trends . A different approach  for the  sentiment analysis of tweets  is to use  non-
-parametric topic mode lling algorithms , as proposed by Si et al. (2013) .  
-In the paper  of Bollen Mao and Zeng  (2011)  the prediction of  the Dow Jones Industrial Average 
-was done  by analysing the  text content of daily Twitter feeds by means of two mood -tracking tools : 
-OpinionFinder – which tracks positive and negative mood s – and Google  Profile of Mood States 
-(GPOMS) , which  measure s language mood in term of six dimensions (Calm, Alert, Sure, Vital, 
-Kind, and Happy) . Rechenthin et al. (2013)  tried to understand if there were financial agents that 
-could manipulate sentiment trend s, to influence stock market price s posting specific, either positive 
-or negative, messages on specialized financial website s. Nguyen et al.  (2014)  analyzed the main 
-topics and sentiment of eighteen  Yahoo!Finance message boards for eighteen  stocks . On message 
-boards, users discussed company news, fa cts or comments (often negative) about specific company 
-events, and personal forecasts. Analysing such platforms can be helpful, also because users have the 
-possibility to annotate message with tags (e.g., Strong Buy, Buy, Hold, Sell and Strong Sell). 
-Auth ors proved that adding sentiment analysis to models based on historical prices  trends  can 
-increase the predictive power of such models.  
-Yang et al. (2015)  demonstrated the existence of Twitter ’s financial communities – which presented 
-a small -world structure  – inferred studying  friend -following relationship s and user profile s, 
-including language preference s, location s, account creation date s and  time zone s. Looking at the 
-sentiment of the tweets sent by  people  in the most central network positions,  the authors could 
-predict  financial market indices.   
-Lastly, other techniques were  also implemented with the aim of improving stock prices prediction s, 
-such as a rtificial neural network s. Patel and Yalamalle  (2014) , for example,  achieved good results 
-using neural networks  to predict stock price s in India.   
-Semantic and social network analysis  of the communication of employees proved their  value for 
-several purposes, such as predicting turnover intentions (Gloor et al., 2017 a), improving customer 6 
- satisfaction (Gloor and Giacomelli, 2014; Gloor et al., 2017 b), or promoting innova tion within, and 
-across, organis ational boundaries  (Wright and Dana, 2003; Dana, Etemad and Wright, 2008; Allen 
-et al., 2016) . These  analyses , which  also stress ed the importance of looking at the communication 
-styles and interactions taking place within the organizational boundaries,  were often carried out  
-using traditional surveys or exploring e -mail networks.  
-Starting from the  insights  that emerge  from past research, we developed t he present  study with the 
-idea of offering a triple contribution: first ly, we present semantic and social network metrics that  
-can be integrated in existing financial models to increase their forecasting accuracy; second ly, we 
-give evidence to the value of transforming text data into words of networks, to extend the 
-informative power of traditional semantic analysis; third ly, we show how the market value of a firm 
-can be at least partially inferred by looking at the internal interactions among employees, when 
-considering an  intranet social network.  
-An intranet is a private network based on web protocols , belonging to an organization, and usually 
-accessible only by the organization's members.  The websites  and software applic ations  of an 
-intranet  look and act just like any others, but the firewall  surrounding an intranet fends off 
-unauthorized access and use  (Beal, 2017) . An intranet  forum is a social network among  all the 
-people  inside a company, in which employees can exchange text messages, share news and 
-comments, or multimedia files.  
-Enterprise intranets proved to offer important insights to business mangers  (Eppler, 2001) , also 
-being a valuable tool to promote  knowledge sharing (Hollingshead, Fulk and Monge, 2002), 
-facilitate HR activities  and assess the internal mood  (Sulaiman, Zailani and Ramayah, 2012) . From 
-an intranet social network it is  often  possible to extract knowledge maps (Eppler, 2001) . These are  
-graph s that provide a visual info rmation about knowledge sources  and help evaluating strengths and 
-weaknesses of knowledge related assets . Intranet s are usefu l also from  a Human Resource  point  of 
-view , to understand role s and skills  within the organization, current activities and possessed 
-knowledge  (DiMicco et al., 2009).  7 
- To the extent of our knowledge, there is no research  trying to combine  words of networks  with 
-semantic and social network  analysis of an intranet social network,  with the aim of forecasting  the 
-stock price  of a company.  
- 
-2.1.  Words  and Networks  
-In this paper, we propose the analysis of a word co -occurrence network , as an addition to traditional 
-semantic analysis. There are  several  studies in this field, for example  Diesner and Carley  (2005)  
-showed how  to detect social structure s through text analysis: by analysing  major newspaper s they 
-managed to discover the social structure of covert networks – terrorist groups operating in the West 
-Bank.  
-Network s of words  can be buil t in different ways, such as the analysis of  words  co-occurrence s in 
-single sentences or text excerpts (Dagan, Marcus and Markovitch, 1995; Bullinaria and Levy, 
-2012) . Centering resonance analysis was also  proposed as a  method for creating a network from a 
-text by analysing its centres  (Corman et al. , 2002) . Other scholars used  techniques based on 
-hypertext s (Trigg and Weiser, 1986)  or semantic webs (Tim and Berners -Lee, 2006; van Atteveldt, 
-2008)  focusing on specific  words and links between  online  pages.   
-In our  explorative  study,  we create d a word co -occurrence network , considering  the messages 
-extracted from the  intranet forum  of a large Italian company  and the co -occurrence of these words . 
-We investigated the position in the graph  of the company brand  – i.e. its centrality measures 
-(Freeman, 1978)  – to better predict  the company stock price  (together with the use of more 
-conventional social network and semantic variables) . 
- 
-3. Case Study  
-In our case study, we were able to fully crawl the intranet forum  of a large Italian  company , with 
-more than 50,000 employees, out of which about 8,000 were actively partic ipating to the online 8 
- discussion s. As per agreed privacy arrangements, w e are prohibited from revealing  more details 
-about the company.  
-In the intranet forum , employees were al lowed to post news and to comment on their own posts or 
-those of others, with no restrictions or pre -approval s from  moderators.  Starting from the analysis of 
-these interaction patterns, we were able to create a first Interaction Network , with n nodes  
-(employees)  and m directed arcs  (posts) . In this network, there is an arc originating at the node i and 
-terminating at the node j, if the social actor i answer s to a comment or a news of the actor j. This 
-network – built considering the news and comments posted between September 2014 and June 
-2016 , for a total of 94 weeks  – comprises  8320 nodes and 48020 links.  
-In addition to studying the interactions  among  people working in the company, we also analysed the 
-relationships among the words used in the posts. In particular, we transformed the text of news and 
-comments into a Word Network , based on words co -occurrences  (Dagan, Marcus and Markovitch, 
-1995;  Bullin aria and Lev y, 2012) . In this network, nodes are representative of single words and arcs 
-connect words that co -occur in the text, either conside ring the words before, or after  a specific one, 
-with a maximum distance of seven words. To put it in other words , for each co -occurrence within 
-this range we created a link going from one word to the other, with a directionality which  respect s 
-the order of appearance in the text. To give an example, if a post is “Hello D olly”, then we would  
-have two nodes – “Hello” and “D olly”  – with an arc originating at the first node and terminating at 
-the second one. Before building the word network, we corrected or removed the misspelled words 
-and removed the stop words  - i.e. most common words in the language which usually do not contain 
-important significance  - using the Python NLTK library (Perkins, 2014) . The resulting network is 
-made of  about 16,00 0 words and more than 6,000,000 links.  
-When building networks of words,  differen t techniques can be used  and the maximum range for 
-considering a co-occurrence can vary. The choice of a maximum interval of 7 words provided the 
-best results in our case study; however , we maintain that the analyst should be free to adjust this 
-interval according to the specific context and dataset analysed.  We also considered the possibility to 9 
- simplify our network , by applying lemming or stemming algorithms to reduce words to their root 
-forms (Korenius et al. , 2004; Perkins, 2014) . However, a test in this direction produced no b etter 
-results than those obtained when we worked  on the full network.  
- 
-3.1. Study Variables  
-Considering the two networks described in the previous section, we extracted  weekly measures for 
-a specific set of variables, representative of the social structure, the activity and the employees’  use 
-of language. To measure structural positions,  we referred to well -known centrality metrics of Social 
-Network Analysis : betweenness and degree centrality (Freeman,  1978) .  
-Degree centrality  measures the number of direct connection of a node in the network  and 
-corresponds to the number of incident arcs, either originating or terminating at that node . 
-Group Degree Centrality  quantifies the variability of individual degree centrality scores and  it is 
-used to measure how much centralized a network  is, and so how much  dominated by a set of highly 
-central nodes. This measure reaches its maximum of 1 for a star graph (Wasserman and Faust, 
-1994) . 
-Betweenness Cen trality  is a measure that reflects the number of times a node lies in -between the 
-shortest paths that connect every other pair of nodes (Wasserman and Faust, 1994) . 
-Group Betweenness Centrality  reflects the heterogeneity of betweenness centrality scores of  single 
-actors: for a star graph the value is maximum and equal to 1; for a network where each actor has the 
-same level of betweenness, this index is equal to zero  (Wasserman and Faust, 1994) . 
-With  regard to the interaction n etwork, we considered group level metrics  with the intent of 
-investigating the full network activity, without focusing on single employees; for the word n etwork, 
-on the other hand, we focused on the  structural  position s of the node associated to the company 
-brand  (which also co rresponds to the company name in the stock market) . Our aim was to test if the 10 
- fluctuations of the brand in this network could be used as a predictor of the  company  stock price  at 
-the end of each week (which is our dependent variable) . 
-To investigate the u se of language by the  employees  in the intranet forum , we carried out a 
-semantic analysis of the content of news and comments, mapping their average  weekly  level of 
-sentiment, emotionality and complexity.  
-Sentiment  is a measure of the positivity or negativity of a text, calculated  by using a multi -lingual 
-classifier based on a machine learning algorithm (trained on large datasets extracted from Twitter) 
-(Brönnimann, 2014) . A score of 0.5 represents a neutral sentime nt, whereas higher scores represent 
-a positive sentiment , and lower scores a negative one.  
-Emotionality  measures the variation in the overall sentiment and it is calculated as its standard 
-deviation. If all the messages converg e towards a positive  sentimen t score,  the emotionality will be 
-low; by contrast , if there is a frequent  alternation of positive and negative messages,  the 
-emotionality will be high  (Brönnimann, 2014) . 
-Complexity  measures the distribution of words within a text, c onsidering as more complex the 
-words that are less frequently used in a specific context  (Brönnimann, 2014) . Accordingly, a rare 
-word that is commonly used in a n intranet forum  will not increase the general level of complexity.  
-As the last predictor in our study, we considered the weekly levels of activity  as the number of 
-messages posted during a week ( Activity ) or the number of word co -occurrences ( Activity Words ), 
-i.e. the nu mber of links in the word network . 
-The semantic analysis, as well  as the computation of the centrality  and activity  measures, were 
-implemented  by using the software Condor1 and the package NLTK developed for the 
-programming language Python 3  (Perkins, 2014) . 
- 
-1 www.galaxyadvisors.com/product -extended.html  11 
- Lastly, to control for the general stock mar ket fluctuations, and  the events which could affect all the 
-market, we included the FTSE MIB in the predictive models . This i ndex is the primary benchmark 
-for the Italian equity market; it captures about 76% of the domestic market capitalization.   
-As per agreed privacy arrangements, we could not collect other control variables, such as 
-employees’ age, gender, job role or busines s department.  
- 
-4. Results  
-We combined the variables  presented in Section 3 to see if they c ould be used as predictors of a 
-company price on the  Italian  stock market. Our aim is not to present a final model to make stock 
-market prediction s, but to draw  the attention of scholar s on new variables that can be extracted from 
-a company intranet forum  and easily integrated in more complex models, to improve the ir 
-predictive power.  
-We started our analysis by looking at the correlation s between our variables  and the company stock 
-price , at various time lags. Specifically, we analysed the insights coming from the intranet forum  
-during the same week in which the stock price was collected  (Current Week ) and in the two weeks 
-before  (named respectively 1 Week Lag  and 2 Week s Lag ), to see which time interval was more 
-informative  (Table 1) . 
- 
- 
- 
- 
- 
- 
- 
- 12 
-  
-Table 1. Pearson’s correlation coefficients with stock price (N=94).  
- 
-For an easier reading , we only reported the correlation coefficients of each variable with the 
-company stock price . As the table shows , there are  relatively high correlation s of group  degree and  
-group betweenne ss centrality wi th price . These  correlations are  negative  at all lags,  suggesting that 
-a more heterogeneous network, less centred on few dominant nodes, is a better indicator of higher 
-prices . As regards the semantic variables, sentiment is the only one which shows a significant 
-correlation (at lag 1 an d 2); this correlation is negative, probably reflecting the fact that a  more 
-technical language  is usually associated to news related to  firm performance and to financial 
-events.  As expected, FTS E MIB is highly correlated with  stock price, proving its val ue as a control 
-variable.  Lastly, the degree centrality of the company brand  in the word network proved to be 
-significantly correlated with price: when the company name is more frequently mentioned in the  
-general  discourse, in association with a higher number of different co -occurring words, the stock 
-price is higher.  To consider possible autocorrelation ef fects , we implemented  a first differencing of 
-the dependent variable  and the Granger causality tests presente d in Table 2 . We considered up to 
-three -week  lags, as suggested by the models diagnostics.  
-  
-Current Week  
-Price  Price 1 Week 
-Lag Price 2 Weeks Lag  
-Activity Words  .421**  .447**  .426**  
-Activity (Interaction Network)  -.128 -.121 -.180 
-Group Betweenness Centrality (Interaction 
-Network)  -.302**  -.256*   -.272**  
-Betweenness Centrality (Word Network)  .142 .107 .283*  
-Complexity (Interaction Network)  .091 .043 -.034 
-Degree Centrality (Word Network)  .369**  .358**  .330**  
-Emotionality (Interaction Network)  -.165 -.194 -.148 
-Sentiment (Interaction Network)  -.185 -.230*  -.264*  
-FTSE MIB  .877**  .863**  .877**  
-Group Degree Centrality (Interaction Network)  -.389**  -.286**  -.252*  
-*p<.05; **p<.01.     13 
- Dependent: Price  2 
-FTSE MIB  3.796 ^ 
-Activity (Interaction Network)  .994^  
-Activity Words  5.246^  
-Group Betweenness Centrality 
-(Interaction Network)  3.984^  
-Betweenness Centrality (Word 
-Network)  3.777^  
-Complexity (Interaction Network)  
-5.806^  
-Degree Centrality (Word Network)  .023^  
-Emotionality (Interaction Network)  1.543^  
-Sentiment (Interaction Network)  .831^  
-Group Degree Centrality (Interaction 
-Network)  1.574^  
-^p > .05.  
- 
-Table 2 Granger causality tests ( Chi-squared values, N=94) . 
- 
-As Table 2  shows , all our predictors can significantly  granger -cause the company stock  price , 
-suggesting that they could all be considered while making new predictive models.  
-As a last step of our analysis, we b uilt multiple regression models  to forecas t the company stock 
-price and comment on the variance explained by each predictor  (Table 3) . We did not use more 
-complex time series models – such as ARIMAX  – because the autocorrelation of the stock price 
-was not significant in the Durbin -Watson test.  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 14 
-  
-Table 3. Stock price prediction: multiple regression models.  
- 
-In the first models, we tested the contribut ion of predictors in blocks,  avoiding to put together 
-measures for which  we found multicollinearity problems ( e.g., group degree and betweenne ss 
-centrality).  In Model 8,  we combined  all the significant variables which c ould better explain the 
-price  variance . With respect to the time  lags of each variable , we chose those who better performed  
-in the models.  We found that the FTSE MIB alone explain ed 75.8% of the price variance. The new 
-predictors  introduced by this study could increase the variance explained by 9.2%. In particular, we 
-found that  negative sentiment  at two weeks lag,  higher activity in the word network at one week 
-lag, higher  betweenne ss centrality of the company brand  and lower  group betweenne ss centrality  
-are all significant indicators of a higher stock price.  Figure 1 summarizes our findings.  
- 
-  
-Model 1  Model 
-2 Model 3  Model 4  Mod
-el 5 Model 
-6 Model 
-7 Model 8  
-FTSE MIB  5.34x10E -
-5**    
-   
-5.88x10E -
-5** 
-Complexity (Interaction 
-Network) Lag 0   
-.029   
-    
-Emotionality (Interaction 
-Network) Lag1   
--.058   
-    
-Sentiment (Interaction 
-Network) Lag2   
--.108*    
-   
--.053**  
-Activity Words Lag1    
-0.222**   
-   
-.076**  
-Activity (Interaction 
-Network) Lag 0     
-1.59x10
-E-5     
-Group Degree Centrality 
-(Interaction Network) 
-Lag 0      -
-.409
-*    
-Group Betweenness 
-Centrality (Interaction 
-Network) Lag2     
--.236*     
--.053*  
-Betweenness Centrality 
-(Word Network) Lag 0      
- .100*   
-.114**  
-Degree Centrality (Word 
-Network) Lag0      
-  
-.001*
-*  
-Constant  -.085 1.081*
-* .899**  1.081**  1.06
-9* .963*
-* .922*
-* -.179**  
- Adjusted R Square d .758 .046 .205 .069 .142 .032 .127 .851 
-*p<.05;
-**p<.01  15 
-  
- 
- 
-Figure 1 . Significant predictors from the intranet social network . 
- 
-5. Discussion  and Conclusions  
-The objective of this paper was to study  the employees’ communication behaviour  on a large 
-company intranet forum , to extract new variables that could be useful to forecast a company’s  stock 
-price . We did not mean to p resent final forecasting models  but to give evidence to the importance of 
-new metr ics that can be integrated in existing financial models to improve their accuracy . In this 
-sense, our work has practical  and theoretical  implications for scholars, financial analysts and 
-professional investors.    
-By means of Granger causality tests, we proved the value of our metrics up to two -week lags. 
-Subsequently, we included our predictors in multiple regression models  to see whether they could 
-improve the models accuracy with respect to  the control va riable FTSE MIB.  
-Our work  is consistent with previous studies linking sentiment analysis and stock prices (Chung and 
-Liu, 2011; Zhang, Fuehres and Gloor, 2011) : we found a negative corre lation of the stock price with 
-the sentiment of messages posted  in the company intranet forum . This cou ld be partially explained 
-by a more technical language used in news which deal with firm performance (with a lower 
-sentiment , for example,  when compared to posts where employees discuss their leisure activities 
-16 
- promoted by the company) . It could be the case that more irrelevant and positive messages  are 
-dominant in the network when there are no big events that can positively affect the stock  price (this 
-hypothesis was partially confirmed by an interview with the people in charge of monitoring the 
-employees’ activity on the intranet forum).  A more “democratic” network structure  (lower group 
-betweenness centrality) , less dominated by a smaller number of social actors, also proved to be 
-predictive of higher prices.  
-We offer a new contribution  since we are among the first scholars linking a company stock price 
-with the communication  behaviours  on its intranet forum . Even if the informative power  of the  
-analysis of the  employees’ activity on enterprise intranets has been discussed in the past ( Eppler, 
-2001; DiMicco et al., 2009) , its association with  the market value of a company remain s mostly  
-unexplored.  We support the id ea that internal  communication behaviours can affect and  partially 
-mirror  business performance  (Gloor et al., 2017 b), thus influencing  a company ’s stock price.  
-An additional contribution of this research is to present a new use of word co-occurrence network s, 
-which  helps financial predictions . We transformed the content  of the messages posted on the 
-intranet forum into a word co -occurrence network  – where every node, representative of a specific 
-word, was linked to the others based on th eir co -occurrences  in messages  (Dagan, Marcus and 
-Markovitch, 1995 ); on this  network , we studied the associations between specific positions of the 
-company  brand and its stock price. Looking at the patt erns interconnecting words, we saw  that a 
-higher activity  – i.e. a denser  graph with more co -occurrences  – and a higher betweenness centrality 
-of the company brand can predict higher prices. This last result  could be representative of cases 
-where the brand is more widely used, spreading ac ross many different news and comments  and 
-therefore being more central in the discourse .   
-Some of the l imitations of this study are due to  our impossibility to analyse  more companies or 
-different  business sector s. Moreover, we could not consider the communication and the interactions 17 
- between employees  which  went  through other media  – such as emails, instant messaging  apps, 
-phone calls or face -to-face communication.  
-To extend our results , we advocate further researc h to study the intranet forum s of more firms, also 
-taking into account  their size and business sector.  It would also be important to consider larger time 
-frames , to test other variables and network construction techniques , and to develop predictive 
-models based on more complex machine learning algorithms (which already proved their value in 
-the past).   
-Due to privacy agreements, we could not access to more control variables to differentiate  the 
-interactions based on the individual differe nces of employees. Therefore, future researchers should 
-consider taking  into account  factors  such as employees’ age, gender and job role . Lastly , it could be 
-interesting to study if more accurate models c an be obtained  by isolating the communication taking 
-place in specific business departments or geographical locations . Depending on the business 
-context,  one could focus the  attention to  the communication of employees  working in specific 
-business units  (customer care, financial department, etc ...). 
- 
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- 

txt/2105.12205.txt

@@ -1,554 +0,0 @@
-arXiv:2105.12205v1  [cs.AI]  25 May 2021A New Score for Adaptive Tests
-in Bayesian and Credal Networks
-Alessandro Antonucci, Francesca Mangili,
-Claudio Bonesana, and Giorgia Adorni
-Istituto Dalle Molle di Studi sull’Intelligenza Artificial e, Lugano, Switzerland
-{alessandro,francesca,claudio.bonesana,giorgia.adorn i}@idsia.ch
-Abstract. Atest is adaptive when its sequence andnumber of questions
-is dynamically tuned on the basis of the estimated skills of t he taker.
-Graphical models, such as Bayesian networks, are used for ad aptive tests
-as they allow to model the uncertainty about the questions an d the skills
-in an explainable fashion, especially when coping with mult iple skills.
-A better elicitation of the uncertainty in the question/ski lls relations
-can be achieved by interval probabilities. This turns the mo del into a
-credalnetwork, thus making more challenging the inferential comp lexity
-of the queries required to select questions. This is especia lly the case for
-the information theoretic quantities used as scoresto drive the adaptive
-mechanism. We present an alternative family of scores, base d on the
-mode of the posterior probabilities, and hence easier to exp lain. This
-makes considerably simpler the evaluation in the credal cas e, without
-significantly affectingthe qualityofthe adaptive process. Numerical tests
-on synthetic and real-world data are used to support this cla im.
-Keywords: computer adaptive tests ·information theory ·credal net-
-works ·Bayesian networks ·index of qualitative variation
-1 Introduction
-A test or an exam can be naturally intended as a measurement proce ss, with the
-questions acting as sensors measuring the skills of the test taker in a particular
-discipline. Such measurement is typically imperfect with the skills modelle d as
-latent variables whose actual values cannot be revealed in a perfec tly reliable
-way. The role of the questions, whose answers are regarded inste ad as mani-
-fest variables, is to reduce the uncertainty about the latent skills. Following this
-perspective, probabilistic models are an obvious framework to desc ribe tests.
-Consider for instance the example in Figure 1, where a Bayesian netw ork evalu-
-ates the probability that the test taker knows how to multiply intege rs. In such
-framework making the test adaptive, i.e., picking a next question on the basis of
-the currentknowledgelevelofthe testtakerisalsoverynatural. The information
-gain for the available questions might be used to select the question le ading to
-the more informative results (e.g., according to Table 1, Q1is more informative
-thanQ2no matter what the answer is). This might also be done before the
-answer on the basis of expectations over the possible alternatives .2 Antonucci et al.
-A critical point when coping with such approaches is to provide a realis tic
-assessment for the probabilistic parameters associated with the m odelling of the
-relationsbetweenthe questionsand the skills.Havingto providesha rpnumerical
-values for these probabilities might be difficult. As the skill is a latent qu antity,
-complete data are not available for a statistical learning and a direct elicitation
-should be typically demanded to experts (e.g., a teacher). Yet, it mig ht be not
-obvious to express such a domain knowledge by single numbers and a m ore
-robust elicitation, such as a probability interval (e.g., P(Q1= 1|S1= 1)∈
-[0.85,0.95]), might add realism and robustness to the modelling process [13].
-With such generalized assessments of the parameters a Bayesian n etwork simply
-becomes a credalnetwork [20]. The counterpart of such increased realism is
-the higher computational complexity characterizing inference in cr edal networks
-[19]. This is an issue especially when coping with information theoretic me asures
-such an information gain, whose computation in credal networks mig ht lead to
-complex non-linear optimization tasks [17].
-The goal of this paper is to investigate the potential of alternative s to the
-information-theoreticscoresdrivingthequestionselectioninadap tivetestsbased
-on directed graphical models, no matter whether these are Bayes ian or credal
-networks. In particular, we consider a family of scores based on th e (expected)
-mode of the posterior distributions over the skills. We show that, wh en coping
-withcredalnetworks,thecomputationofthesescorescanbere ducedtoasimpler
-sequenceoflinearprogrammingtask.Moreover,weshowthatthe sescoresbenefit
-of better explainability properties, thus allowing for a more transpa rent process
-in the question selection.
-P(Q1= 1|S= 1) = 0 .9
-P(Q1= 1|S= 0) = 0 .3
-P(Q2= 1|S= 1) = 0 .6
-P(Q2= 1|S= 0) = 0 .4Knows multiplication ( S)
-10×5? (Q1) 13×14?(Q2)
-Fig.1.A Bayesian network over Boolean variables modelling a simpl e test to evaluate
-integer multiplication skill with two questions.
-The paper is organized as follows. A critical discussion about the exis ting
-work in this area is in Section 2. The necessary background material is reviewed
-in Section 3. The adaptive testing concepts are introduced in Sectio n 4 and
-specialized to graphical models in 5. The technical part of the paper is in Section
-6, where the new scores are discussed and specialized to the creda l case, while
-the experiments are in Section 7. Conclusions and outlooks are in Sec tion 8.A New Score for Adaptive Tests in Bayesian and Credal Network s 3
-Table 1. Posterior probabilities of the skill after one or two questi ons in the test based
-on the Bayesian network in Figure 1. A uniform prior over the s kill is considered.
-Probabilities are regarded as grades and sorted from the low est one. Bounds obtained
-with a perturbation ǫ=±0.05 of all the input parameters are also reported.
-Q1Q2P(S= 1|q1,q2)P(S= 1|q1,q2)P(S= 1|q1,q2)
-0 0 0 .087 0 .028 0 .187
-0− 0.125 0 .052 0 .220
-0 1 0 .176 0 .092 0 .256
-−0 0 .400 0 .306 0 .506
-−1 0 .600 0 .599 0 .603
-1 0 0 .667 0 .626 0 .708
-1− 0.750 0 .748 0 .757
-1 1 0 .818 0 .784 0 .852
-2 Related Work
-Modelling atest asa processrelatinglatent and manifest variablessin ce the clas-
-sicalitem response theory (IRT), that has been widely used even to implement
-adaptive sequences [12]. Despite its success related to the easeof implementation
-and inference, IRT might be inadequate when coping with multiple laten t skills,
-especially when these are dependent. This moved researcherstow ards the area of
-probabilistic graphical models [15], as practical tools to implement IR T in more
-complex setups [2]. Eventually, Bayesian networks have been even tually iden-
-tified as a suitable formalism to models tests, even behind the IRT fra mework
-[23], this being especially the case for adaptive models [24] and coach ed solving
-[10]. In order to cope with latent skills, some authors successfully ad opted EM
-approaches to these models [21], this also involving the extreme situa tion of no
-ground truth information about the answers [5]. As an alternative a pproach to
-the same issue, some authors considered relaxations of the Bayes ian formalism,
-such as fuzzy models [6] and imprecise probabilities [17]. The latter is the di-
-rection we consider here, but trying to overcome the computation al limitations
-of that approach when coping with information-theoretic scores. This has some
-analogy with the approach in [9], that is focused on the Bayesian case only, but
-whose score, based on the same-decision problem, appears hard to be extended
-to the imprecise framework without affecting the computational co mplexity.
-3 Background on Bayesian and Credal Networks
-We denote variables by Latin uppercase letters, while using lowercas e for their
-generic values, and calligraphic for the set of their possible values. T hus,v∈ V
-is a possible value of V. Here we only consider discrete variables.1
-1IRT uses instead with continuous skills. Yet, when coping pr obabilistic models, hav-
-ing discrete skill does no prevent evaluations to range over a continuous domain.
-E.g., see Table 1, where the grade corresponds to a (continuo us) probability.4 Antonucci et al.
-3.1 Bayesian Networks
-A probability mass function (PMF) over Vis denoted as P(V), whileP(v) is
-the probability assigned to state v. Given a function fofV, its expectation with
-respect to P(V) isEP(f) :=/summationtext
-v∈VP(v)f(v). The expectation of −logb[P(V)] is
-calledentropyand denoted also as H(X).2We setb:=|V|to have the maximum
-of the entropy, achieved for uniform PMFs, equal to one.
-Given joint PMF P(U,V), the marginal PMF P(V) is obtained by sum-
-ming out the other variable, i.e., P(v) =/summationtext
-u∈UP(u,v). Conditional PMFs such
-asP(U|v) are similarly obtained by Bayes’s rule, i.e., P(u|v) =P(u,v)/P(v)
-provided that P(v)>0. Notation P(U|V) :={P(U|v)}v∈Vis used for such con-
-ditional probability table (CPT). The entropy of a conditional PMF is d efined
-as in the unconditional case and denoted as H(U|v). The conditional entropy
-is a weighted average of entropies of the conditional PMFs, i.e., H(U|V) :=/summationtext
-v∈VH(U|v)P(v). IfP(u,v) =P(u)P(v) for each u∈ Uandv∈ V, variables
-UandVare independent. Conditional formulations are also considered.
-We assume the set of variables V:= (V1,...,V r) to be in one-to-one corre-
-spondence with a directed acyclic graph G. For each V∈V, the parents of V,
-i.e., the predecessors of VinG, are denoted as Pa V. GraphGtogether with the
-collection of CPTs {P(V|PaV)}V∈Vprovides a Bayesian network (BN) specifi-
-cation [15]. Under the Markov condition, i.e., every variable is condition ally in-
-dependent of its non-descendants non-parents given its parent s, a BN compactly
-defines a joint PMF P(V) that factorizes as P(v) =/producttext
-V∈VP(v|paV). Inference,
-intended as the computation of the posterior PMF of a single (querie d) variables
-given some evidence about other variables, is in general NP-hard, b ut exact and
-approximate schemes are available (see [15] for details).
-3.2 Credal Sets and Credal Networks
-A set of PMFs over Vis denoted as K(V) and called credal set (CS). Expec-
-tations based on CSs are the bounds of the PMF expectations with r espect to
-the CS. Thus E[f] := inf P(V)∈K(V)E[f] and similarly for the supremum E. Ex-
-pectations of events are in particular called lower and upper probab ilities and
-denoted as PandP. Notation K(U|v) is used for a set of conditional CSs, while
-K(U|V) :={K(U|v)}v∈Vis a credal CPT (CCPT).
-Analogously to a BN, a credal network (CN) is specified by graph Gtogether
-with a family of CCPTs {K(V|PaV)}V∈V[11]. A CN defines a joint CS K(V)
-corresponding to all the joint PMFs induced by BNs whose CPTs are c onsis-
-tent with the CN CCPTs. For CNs, we intend inference as the comput ation of
-the lower and upper posterior probabilities. The task generalizes BN inference
-being therefore NP-hard, see [19] for a deeper characterization . Yet, exact and
-approximate schemes are also available to practically compute infere nces [4].
-2We set 0·logb0 = 0 to cope with zero probabilities.A New Score for Adaptive Tests in Bayesian and Credal Network s 5
-4 Testing Algorithms
-Atypicaltestaimsatevaluatingthe knowledgelevelofatesttaker σonthe basis
-of her answers to a number of questions. Let Qdenote a repository of questions
-availabletothe instructor.The orderand the number ofquestions pickedfrom Q
-to be asked to σmight not be defined in advance. We call testing algorithm (TA)
-a procedure taking care of the selection of the sequence of quest ions asked to the
-test taker, and to decide when the test stops. Algorithm 1 depicts a general TA
-scheme, with edenoting the array of the answers collected from test taker σ.
-Algorithm 1 General TA: given the profile σand repository Q, an evaluation
-based on answers eis returned.
-1:e←∅
-2:while not Stopping (e)do
-3:Q∗←Pick(Q,e)
-4:q∗←Answer(Q∗,σ)
-5:e←e∪{Q∗=q∗}
-6:Q←Q\{Q∗}
-7:end while
-8:returnEvaluate (e)
-Boolean function Stopping decides whether the test should end, this choice
-being possibly based on the previous answers in e. Trivial stopping rules might
-be based on the number of questions asked to the test takes ( Stopping (e) = 1
-if and only if |e|> n) or on the number of correct answers provided that a
-maximum number of questions is not exceeded. Function Pickselects instead
-the question to be asked to the student from the repository Q. A TA is called
-adaptive when this function takes into account the previous answers e. Trivial
-non-adaptive strategies might consist in randomly picking an element ofQor
-following a fixed order. Function Answeris simply collecting (or simulating) the
-answeroftest taker σtoaparticularquestion Q. In ourassumptions,this answer
-is not affected by the previous answers to other questions.3
-Finally,Evaluate is a function returning the overall judgement of the test
-(e.g., a numerical grade or a pass/fail Boolean) on the basis of all th e answers
-collected after the test termination. Trivial examples of such func tions are the
-percentage of correct answers or a Boolean that is true when a su fficient number
-of correct answers has been provided. Note also that in our assum ptions the TA
-isexchangeable , i.e., the stopping rule, the question finder and the evaluation
-function are invariant with respect to permutations in e[22]. In other words,
-3Generalized setupswhere thequalityofthestudentanswer i s affectedbytheprevious
-answers will be discussed at the end of the paper. This might i nclude a fatigue
-model negatively affecting the quality of the answers when ma ny questions have
-been already answered as well as the presence of revealing questions that might
-improve the quality of other answers [16].6 Antonucci et al.
-the same next question, the same evaluation and the same stopping decision is
-produced for any two students, who provided the same list of answ ers in two
-different orders.
-A TA is supposed to achieve reliable evaluation of taker σfrom the answers
-e. As each answer is individually assumed to improve such quality, asking all the
-questions, no matter the order because of the exchangeability as sumption, is an
-obvious choice. Yet, this might be impractical (e.g., because of time lim itations)
-or just provide an unnecessary burden to the test taker. The go al of a good TA
-is therefore to trade off the evaluation accuracy and the number o f questions.4
-5 Adaptive Testing in Bayesian and Credal Networks
-The general TA setup in Algorithm 1 can be easily specialized to BNs as f ollows.
-First, we identify the profile σof the test taker with the actual states of a
-number of latent discrete variables, called skills. LetS={Si}n
-j=1denote these
-skill variables, and sσthe actual values of the skills for the taker. Skills are
-typically ordinal variables, whose states corresponds to increasin g knowledge
-levels. Questions in Qare still described as manifest variables whose actual
-values are returned by the answerfunction. This is achieved by a (possibly
-stochastic) function of the actual profile sσ. This reflects the taker perspective,
-while the teacher has clearly no access to sσ. As a remark, note that we might
-often coarsenthe set ofpossible values Qfor eachQ∈Q: for instance, amultiple
-choicequestionwiththreeoptionsmighthaveasinglerightanswer,t hetwoother
-answers being indistinguishable from the evaluation point of view.5
-A joint PMF over the skills Sand the questions Qis supposed to be avail-
-able. In particular we assume this to correspond to a BN whose grap h has the
-questions as leaf nodes. Thus, for each Q∈Q,PaQ⊆Sand we call PaQ
-thescopeof question Q. Note that this assumption about the graph is simply
-reflecting a statement about the conditional independence betwe en (the answer
-to) a question and all the other skills and questions given scope of th e ques-
-tion. This basically means that the answers to other questions are n ot directly
-affecting the answer to a particular question, and this naturally follo ws from the
-exchangeability assumption.6
-As the available data are typically incomplete because of the latent na ture
-of the skills, dedicated learning strategies, such as various form of constrained
-EM should be considered to train a BN from data. We refer the reade r to the
-variouscontributionsofPlajnerand Vomlel in this field (e.g.,[21]) for acomplete
-discussion of that approach. Here we assume the BN quantification available.
-4In some generalized setups, other elements such as a serendipity in choice in order
-to avoid tedious sequences of questions might be also consid ered [7].
-5The case of abstention to an answer and the consequent problem of modelling the
-incompleteness is a topic we do not consider here for the sake of conciseness. Yet,
-general approaches based on the ideas in [18] could be easily adopted.
-6Moving to other setups would not be really critical because o f the separation prop-
-erties of observed nodes in Bayesian and credal networks, se e for instance [3,8].A New Score for Adaptive Tests in Bayesian and Credal Network s 7
-In such a BN framework, Stopping (e) might be naturally based on an eval-
-uation of the posterior PMF P(S|e), this being also the case for Evaluate . Re-
-garding the question selection, Pickmight be similarly based on the (posterior)
-CPTP(S|Q,e), whose values for the different answers to Qmight be weighted
-by the marginal P(Q|e). More specifically, entropies and conditional entropies
-are considered by Algorithm 2, while the evaluation is based on a condit ional
-expectation for a given utility function.
-Algorithm 2 Information Theoretic TA in BN over the questions Qand the
-skillsS: given the student profile sσ, the algorithms returns an evaluation cor-
-responding to the expectation of an evaluation function fwith respect to the
-posterior for the skills given the answers e.
-1:e=∅
-2:whileH(S|e)> H∗do
-3:Q∗←argmax Q∈Q[H(S|e)−H(S|Q,e)]
-4:q∗←Answer(Q∗,sσ)
-5:e←e∪{Q∗=q∗}
-6:Q←Q\{Q∗}
-7:end while
-8:returnEP(S|e)[f(S)]
-When no data are available for the BN training, elicitation techniques s hould
-beconsideredinstead.AsalreadydiscussedCNsmightofferabette rformalismto
-capture domain knowledge, especially by providing interval-valued pr obabilities
-instead of sharp values. If this is the case, a CN version of Algorithm 2 can be
-equivalentlyconsidered.MovingtoCNsisalmostthesame,providedt hatbounds
-on the entropy are used instead for decisions. Yet, the price of su ch increased
-realism in the elicitation is the higher complexity characterizinginferen ces based
-on CNs. The work in [17] offers a critical discussion of those issues, that are
-only partially addressed by heuristic techniques used there to appr oximate such
-bounds. In the next section we consider an alternative approach t o cope with
-CNs and adaptive TAs based on different scores used to select the q uestions.
-6 Coping with the Mode
-Following[25], we can regardthe PMF entropy(and its conditionalver sion)used
-by Algorithm 2 as an example of index of qualitative variation (IQV). An IQV
-is just a normalized number that takes value zero for degenerate P MFs, one on
-uniform ones, being independent on the number of possible states ( and samples
-for empirical models). The closer to uniform is the PMF, the higher is t he index
-and vice versa.
-In order to bypass the computational issues related to its applicat ion with
-CNs and the explainability limits with both BNs and CNs, we want to consid er8 Antonucci et al.
-alternative IQVs to replace entropy in Algorithm 2. Wilkox deviation from the
-mode(DM) appears a sensible option. Given PMF P(V), this corresponds to:
-M(V) := 1−/summationdisplay
-v∈Vmaxv′∈VP(v′)−P(v)
-|V|−1. (1)
-It is a trivial exercise to check that this is a proper IQV, with the sam e unimodal
-behaviour of the entropy. In terms of explainability, being a linear fu nction of
-the modal probability, the numerical value of the DM offers a more tr ansparent
-interpretation than the entropy. From a computational point of v iew, for both
-marginaland unconditional PMFs, both the entropy and the DM can be directly
-obtained from the probabilities of the singletons.
-The situation is different when computing the bounds of these quant ities
-with respect to a CS. The bounds of M(V) are obtained from the upper and
-lower probabilities of the singletons by simple algebra, i.e,
-M(V) := max
-P(V)∈K(V)M(V) :=|V|−maxv′∈VP(v′)
-|V|−1, (2)
-and analogously with the lower probabilities for M(V). Maximizing entropy
-requires instead a non-trivial, but convex, optimization. See for ins tance [1] for
-an iterative procedure to find such maximum when coping with CSs defi ned by
-probability intervals. The situation is even more critical for the minimiz ation,
-that has been proved to be NP-hard in [26].
-The optimization becomes even more challenging for conditional entr opies,
-there basically are mixtures of conditional entropies based on impre cise weights.
-Consequently, in [17], only inner approximation for the upper bound h ave been
-derived.ThesituationisdifferentforconditionalDMs.Thefollowingr esultoffers
-a feasible approachin a simplified setup, to be later extended to the g eneralcase.
-Theorem 1. Under the setup of Section 5, consider a CN with a single skill
-Sand a single question Q, that is a child of S. LetK(S)andK(Q|S)be the
-CCPTs of such CN. Let also Q={q1,...,qn}andS={s1,...,sm}. The upper
-conditional DM, i.e.,
-M(S|Q) :=|S|− max
-P(S)∈K(S)
-P(Q|S)∈K(Q|S)/summationdisplay
-i=1,...,n/bracketleftbigg
-max
-j=1,...,mP(sj|qi)/bracketrightbigg
-P(qi),(3)
-whose normalizing denominator was omitted for the sake of br evity, is such that:
-M(S|Q) :=m−max
-ˆji=1,...,m
-i=1,...,nΩ(ˆj1,...,ˆjn), (4)A New Score for Adaptive Tests in Bayesian and Credal Network s 9
-whereΩ(ˆj1,...,ˆjn)is the solution of the following linear programming task her e
-below.
-max/summationdisplay
-jxij
-s.t./summationdisplay
-ijxij= 1 (5)
-xij≥0 ∀i,j (6)
-/summationdisplay
-ixij≥P(sj)∀j (7)
-/summationdisplay
-ixij≤P(sj)∀j (8)
-P(qi|sj)/summationdisplay
-ixij≤xij ∀i,j (9)
-P(qi|sj)/summationdisplay
-ixij≥xij ∀i,j (10)
-xiˆji≥xij ∀i,j (11)
-Note that the bounds on the sums over the indexes and on the uni versal quanti-
-fiers are also omitted for the sake of brevity.
-Proof. Equation (3)rewrites as:
-M(S|Q) =m−max
-P(S)∈K(S)
-P(Q|S)∈K(Q|S)n/summationdisplay
-i=1/bracketleftbigg
-max
-j=1,...,mP(sj)P(qi|sj)/bracketrightbigg
-.(12)
-Let us define the variables of such constrained optimization task as:
-xij:=P(sj)·P(qi|sj). (13)
-for each i= 1,...,nandj= 1,...,m. Let us show how the CCPT constraints
-can be easily reformulated with respect to such new variable s by simply noticing
-thatxij=P(sj,qi), and hence P(si) =/summationtext
-ixijandP(qj|si) =xij/(/summationtext
-kxkj).
-Consequently, the interval constraints on P(S)corresponds to the linear con-
-straints in Equations (7)and(8). Similarly, for P(Q|S), we obtain:
-P(qi|sj)≤xij/summationtext
-kxkj≤P(qi|sj), (14)
-that easily gives the linear constraints in Equations (9)and(10). The non-
-negativity of the probabilities corresponds to Equation (6), while Equation (5)
-gives the normalization of P(S)and the normalization of P(Q|S)is by con-
-struction. Equation (12)rewrites therefore as:
-M(S|Q) =m−max
-{vij}ij∈Γ/summationdisplay
-imax
-jxij, (15)10 Antonucci et al.
-whereΓdenotes the linear constraints in Equations (5)-(10). If we set
-ˆji:= argmax
-jxij, (16)
-Equation (15)rewrites as
-M(S|Q) = max
-{vij}ij∈Γ′/summationdisplay
-ixiˆji, (17)
-whereΓ′are the constraints in Γwith the additional (linear) constraints in
-Equation (11), that are implementing Equation (16).
-The optimization on the right-hand side of Equation (17)is not a linear
-programming task, as the values of the indexes ˆjicannot be decided in advance
-being potentially different for different assignments of the optimization variables
-consistent with the constraints in Γ. Yet,we might address such optimization as a
-brute-force task with respect to all the possible assignati on of the indexes ˆji. This
-is exactly what is done by Equation (4)where all the mnpossible assignations
-are considered. This proves the thesis. ⊓ ⊔
-An analogous result with the linear programming tasks minimizing the sa me
-objectivefunctionswithexactlythesameconstraintsallowstocom puteM(S|Q).
-The overall complexity is clearly O(mn) withn:=|Q|. This means quadratic
-complexity for any test where only the difference between a wrong a nd a right
-answer is considered from an elicitation perspective, and tractable computations
-providedthatthenumberofpossibleanswerstothesamequestion wedistinguish
-is bounded by a small constant. Coping with multiple answers becomes trivial
-by means of the results in [3], that allows to merge multiple observed c hildren
-into a single one. Finally, the case of multiple skills might be similarly conside red
-by using the marginal bounds of the single skills in Equations (7) and (8 ).
-7 Experiments
-In this section we validate the ideas outlined in the previous section in o rder to
-check whether or not the DM can be used for TAs as a sensible altern ative to
-information-theoreticscoressuchastheentropy.IntheBNcon text,thisissimply
-achieved by computing the necessary updated probabilities, while Th eorem 1 is
-used instead for CNs.
-7.1 Single-Skill Experiments on Synthetic Data
-For a very first validation of our approach, we consider a simple setu p made of a
-single Boolean skill Sand a repository with 18 Boolean questions based on nine
-different parametrizations (two questions for parametrization). In such BN, the
-CPT of a question can be parametrized by two numbers. E.g., in the ex ample
-in Figure 1, we used the probabilities of correctly answering the ques tion givenA New Score for Adaptive Tests in Bayesian and Credal Network s 11
-that the skill is present or not, i.e., P(Q= 1|S= 1) and P(Q= 1|S= 0). A
-more interpretable parametrization can be obtained as follows:
-δ:= 1−1
-2[P(Q= 1|S= 1)+P(Q= 1|S= 0)], (18)
-κ:=P(Q= 1|S= 1)−P(Q= 1|S= 0). (19)
-Note that P(Q= 1|S= 1)> P(Q= 1|S= 0) is an obvious rationality con-
-straint for questions, otherwise having the skill would make less likely to answer
-properly to a question. Both parameters are therefore non-neg ative. Parameter
-δ, corresponding to the (arithmetic) average of the probability for a wrong an-
-swer over the different skill values, can be regarded as a normalized index of
-the question difficulty . E.g., in Figure 1, Q1(δ= 0.4) is less difficult than Q2
-(δ= 0.5). Parameter κcan be instead regarded as a descriptor of the differ-
-ence of the conditional PMFs associated with the different skill value s. In the
-most extreme case κ= 1, the CPT P(Q|S) is diagonal implementing an iden-
-tity mapping between the skill and the question. We therefore rega rdκas a
-indicator of the discriminative power of the question. In our tests, for the BN
-quantification, we consider the nine possible parametrizations corr esponding to
-(δ,γ)∈[0.4,0.5,0.6]2. ForP(S) we use instead a uniform quantification. For the
-CN approach we perturb all the BN parameters with ǫ=±0.05, thus obtaining
-a CN quantification. A group of 1024 simulated students, half of the m having
-S= 0 and half with S= 1 is used for simulations. The student answers are
-sampled from the CPT of the asked question on the basis of the stud ent profile.
-Figure 2 (left) depicts the accuracy of the BN and CN approaches b ased on
-both the entropy and the DM scores. For credal models, decisions are based on
-the mid-point between the lower and the upper probability, while lower entropy
-and conditional entropies are used. We notably see all the adaptive approaches
-outperforming a non-adaptive, random, choice of the questions. To better in-
-vestigate the strong overlap between these trajectories, in Figu re 2 (right) we
-compute the Brier score and we might observe the strong similarity b etween
-DM and entropy approaches in both the Bayesian and the credal ca se, with the
-credal approaches slightly outperforming the Bayesian ones.
-7.2 Multi-Skill Experiments on Real Data
-For a validation on real data, we consider an online German language p lacement
-test (see also [17]). Four different Booleanskills associated with differ ent abilities
-(vocabulary, communication, listening and reading) are considered and modeled
-by a chain-shaped graph, for which BN and CN quantification are alre ady avail-
-able. A repository of 64 Boolean questions, 16 for each skill, with fou r different
-levels of difficulty and discriminative power, have been used.
-Experiments have been achieved by means of the CREMA library for c redal
-networks [14].7The Java code used for the simulations is available together with
-the Python scripts used to analyze the results and the model spec ifications.8
-7github.com/IDSIA/crema
-8github.com/IDSIA/adaptive-tests12 Antonucci et al.
-0 5 10 15 2000.20.40.60.81
-Number of questionsAccuracy
-0 5 10 15 2000.10.20.30.40.5
-Number of questionsBrier DistanceCredal Entropy
-Credal Mode
-Random
-Bayesian Entropy
-Bayesian Mode
-Fig.2.Accuracy (left) and Brier distance (right) of TAs for a singl e-skill BN/CN
-Performancesare evaluated as for the previous model, the only diff erence be-
-ing that here the accuracy is aggregated by average over the sep arate accuracies
-for the four skills. The observed behaviour, depicted in Figure 3, is a nalogous
-to that of the single skill case: entropy-based and mode-based sc ores are provid-
-ing similar results, with the credal approach typically leading to more a ccurate
-evaluations (or evaluations of the same quality with fewer questions ).
-0 10 20 30 40 50 6000.20.40.60.81
-Number of questionsAggregated AccuracyCredal Entropy
-Credal Mode
-Random
-Bayesian Entropy
-Bayesian Mode
-Fig.3.Aggregated Accuracy for a multi-skill TA
-8 Outlooks and Conclusions
-A new score for adaptive testing in Bayesian and credal networks h as been
-proposed. Our proposal is based on indexes of qualitative variation , being in
-particular focused on the modal probability for their explainability fe atures. An
-algorithm to evaluate this quantity in the credal case is derived. Our experi-
-ments show that moving to these scores does not really affect the q uality of the
-selection process. Besides a deeper experimental validation, a nec essary future
-work consists in the derivation of simpler elicitation strategies for th ese model
-in order to promote their application to real-world testing environme nts.A New Score for Adaptive Tests in Bayesian and Credal Network s 13
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txt/2106.07286.txt

@@ -1,970 +0,0 @@
-This paper has been accepted for publication at the
-IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, 2021. ©IEEE
-Time Lens: Event-based Video Frame Interpolation
-Stepan Tulyakov*;1Daniel Gehrig;2Stamatios Georgoulis1Julius Erbach1
-Mathias Gehrig2Yuanyou Li1Davide Scaramuzza2
-1Huawei Technologies, Zurich Research Center
-2Dept. of Informatics, Univ. of Zurich and Dept. of Neuroinformatics, Univ. of Zurich and ETH Zurich
-Figure 1: Qualitative results comparing our proposed method, Time Lens, with DAIN [3] and BMBC [29]. Our method can
-interpolate frames in highly-dynamic scenes, such as while spinning an umbrella (top row) and bursting a balloon (bottom
-row). It does this by combining events (b) and frames (a).
-Abstract
-State-of-the-art frame interpolation methods generate
-intermediate frames by inferring object motions in the
-image from consecutive key-frames. In the absence of
-additional information, first-order approximations, i.e.
-optical flow, must be used, but this choice restricts the
-types of motions that can be modeled, leading to errors
-in highly dynamic scenarios. Event cameras are novel
-sensors that address this limitation by providing auxiliary
-visual information in the blind-time between frames. They
-asynchronously measure per-pixel brightness changes and
-do this with high temporal resolution and low latency.
-Event-based frame interpolation methods typically adopt a
-synthesis-based approach, where predicted frame residuals
-are directly applied to the key-frames. However, while these
-approaches can capture non-linear motions they suffer
-from ghosting and perform poorly in low-texture regions
-with few events. Thus, synthesis-based and flow-based
-approaches are complementary. In this work, we introduce
-*indicates equal contributionTime Lens , a novel method that leverages the advantages of
-both. We extensively evaluate our method on three synthetic
-and two real benchmarks where we show an up to 5.21
-dB improvement in terms of PSNR over state-of-the-art
-frame-based and event-based methods. Finally, we release
-a new large-scale dataset in highly dynamic scenarios,
-aimed at pushing the limits of existing methods.
-Multimedia Material
-The High-Speed Event and RGB (HS-ERGB) dataset
-and evaluation code can be found at: http://rpg.ifi.
-uzh.ch/timelens
-1. Introduction
-Many things in real life can happen in the blink of an
-eye. A hummingbird flapping its wings, a cheetah accel-
-erating towards its prey, a tricky stunt with the skateboard,
-or even a baby taking its first steps. Capturing these mo-
-ments as high-resolution videos with high frame rates typi-arXiv:2106.07286v1  [cs.CV]  14 Jun 2021cally requires professional high-speed cameras, that are in-
-accessible to casual users. Modern mobile device producers
-have tried to incorporate more affordable sensors with sim-
-ilar functionalities into their systems, but they still suffer
-from the large memory requirements and high power con-
-sumption associated with these sensors.
-Video Frame Interpolation (VFI) addresses this problem,
-by converting videos with moderate frame rates high frame
-rate videos in post-processing. In theory, any number of
-new frames can be generated between two keyframes of
-the input video. Therefore, VFI is an important problem
-in video processing with many applications, ranging from
-super slow motion [10] to video compression [42].
-Frame-based interpolation approaches relying solely
-on input from a conventional frame-based camera that
-records frames synchronously and at a fixed rate. There are
-several classes of such methods that we describe below.
-Warping-based approaches [20, 10, 44, 21, 29] combine
-optical flow estimation [8, 16, 36] with image warping [9],
-to generate intermediate frames in-between two consecutive
-key frames. More specifically, under the assumptions of lin-
-ear motion and brightness constancy between frames, these
-works compute optical flow and warp the input keyframe(s)
-to the target frame, while leveraging concepts, like contex-
-tual information [20], visibility maps [10], spatial trans-
-former networks [44], forward warping [21], or dynamic
-blending filters [29], to improve the results. While most of
-these approaches assume linear motion, some recent works
-assume quadratic [43] or cubic [5] motions. Although these
-methods can address non-linear motions, they are still lim-
-ited by their order, failing to capture arbitrary motion.
-Kernel-based approaches [22, 23] avoid the explicit mo-
-tion estimation and warping stages of warping-based ap-
-proaches. Instead, they model VFI as local convolution over
-the input keyframes, where the convolutional kernel is esti-
-mated from the keyframes. This approach is more robust to
-motion blur and light changes. Alternatively, phase-based
-approaches [18] pose VFI as a phase shift estimation prob-
-lem, where a neural network decoder directly estimates the
-phase decomposition of the intermediate frame. However,
-while these methods can in theory model arbitrary motion,
-in practice they do not scale to large motions due to the lo-
-cality of the convolution kernels.
-In general, all frame-based approaches assume simplis-
-tic motion models (e.g. linear) due to the absence of vi-
-sual information in the blind-time between frames, which
-poses a fundamental limitation of purely frame-based VFI
-approaches. In particular, the simplifying assumptions rely
-on brightness and appearance constancy between frames,
-which limits their applicability in highly dynamic scenar-
-ios such as ( i) for non-linear motions between the input
-keyframes, ( ii) when there are changes in illumination or
-motion blur, and ( iii) non-rigid motions and new objects ap-pearing in the scene between keyframes.
-Multi-camera approaches. To overcome this limita-
-tion, some works seek to combine inputs from several
-frame-based cameras with different spatio-temporal trade-
-offs. For example, [1] combined low-resolution video with
-high resolution still images, whereas [25] fused a low-
-resolution high frame rate video with a high resolution low
-frame rate video. Both approaches can recover the miss-
-ing visual information necessary to reconstruct true object
-motions, but this comes at the cost of a bulkier form factor,
-higher power consumption, and a larger memory footprint.
-Event-based approaches. Compared to standard frame-
-based cameras, event cameras [14, 4] do not incur the afore-
-mentioned costs. They are novel sensors that only report the
-per-pixel intensity changes, as opposed to the full intensity
-images and do this with high temporal resolution and low
-latency on the order of microseconds. The resulting output
-is an asynchronous stream of binary “events” which can be
-considered a compressed representation of the true visual
-signal. These properties render them useful for VFI under
-highly dynamic scenarios (e.g. high-speed non-linear mo-
-tion, or challenging illumination).
-Events-only approaches reconstruct high frame rate
-videos directly from the stream of incoming events using
-GANs [38], RNNs [32, 33, 34], or even self-supervised
-CNNs [28], and can be thought of as a proxy to the VFI
-task. However, since the integration of intensity gradients
-into an intensity frame is an ill-posed problem, the global
-contrast of the interpolated frames is usually miscalculated.
-Moreover, as in event cameras intensity edges are only ex-
-posed when they move, the interpolation results are also de-
-pendent on the motion.
-Events-plus-frames approaches. As certain event cam-
-eras such as the Dynamic and Active VIsion Sensor
-(DA VIS) [4] can simultaneously output the event stream and
-intensity images – the latter at low frame rates and prone
-to the same issues as frame-based cameras (e.g. motion
-blur) – several works [26, 41, 11, 37] use both streams of
-information. Typically, these works tackle VFI in conjunc-
-tion with de-blurring, de-noising, super-resolution, or other
-relevant tasks. They synthesize intermediate frames by
-accumulating temporal brightness changes, represented by
-events, from the input keyframes and applying them to the
-key frames. While these methods can handle illumination
-changes and non-linear motion they still perform poorly
-compared to the frame-based methods (please see § 3.2),
-as due to the inherent instability of the contrast threshold
-and sensor noise, not all brightness changes are accurately
-registered as events.
-Our contributions are as follows
-1. We address the limitations of all aforementioned
-methods by introducing a CNN framework, named
-Time Lens , that marries the advantages of warping-Figure 2: Proposed event-based VFI approach.
-and synthesis-based interpolation approaches. In our
-framework, we use a synthesis-based approach to
-ground and refine results of high-quality warping-
-based approach and provide the ability to handle illu-
-mination changes and new objects appearing between
-keyframes (refer Fig. 7),
-2. We introduce a new warping-based interpolation ap-
-proach that estimates motion from events, rather than
-frames and thus has several advantages: it is more ro-
-bust to motion blur and can estimate non-linear mo-
-tion between frames. Moreover, the proposed method
-provides a higher quality interpolation compared to
-synthesis-based methods that use events when event
-information is not sufficient or noisy.
-3. We empirically show that the proposed Time Lens
-greatly outperforms state-of-the-art frame-based and
-event-based methods, published over recent months,
-on three synthetic and two real benchmarks where we
-show an up to 5.21 dB improvement in terms of PSNR.
-2. Method
-Problem formulation. Let us assume an event-based
-VFI setting, where we are given as input the left I0and
-rightI1RGB key frames, as well as the left E0!and right
-E!1event sequences , and we aim to interpolate (one or
-more) new frames ^Iat random timesteps in-between the
-key frames. Note that, the event sequences ( E0!,E!1)
-contain all asynchronous events that are triggered from the
-moment the respective (left I0or rightI1) key RGB frame
-is synchronously sampled, till the timestep at which we
-want to interpolate a new frame ^I. Fig. 2 illustrates the
-proposed event-based VFI setting.
-System overview. To tackle the problem under consid-
-eration we propose a learning-based framework, namely
-Time Lens , that consists of four dedicated modules that
-serve complementary interpolation schemes, i.e. warping-
-based and synthesis-based interpolation. In particular, (1)
-thewarping-based interpolation module estimates a new
-frame by warping the boundary RGB keyframes using op-
-tical flow estimated from the respective event sequence; (2)
-thewarping refinement module aims to improve this esti-
-mate by computing residual flow; (3) the interpolation by
-synthesis module estimates a new frame by directly fusing
-the input information from the boundary keyframes and the
-event sequences; finally (4) the attention-based averaging
-module aims to optimally combine the warping-based andsynthesis-based results. In doing so, Time Lens marries
-the advantages of warping- and synthesis-based interpola-
-tion techniques, allowing us to generate new frames with
-color and high textural details while handling non-linear
-motion, light changes, and motion blur. The workflow of
-our method is shown in Fig. 3a.
-All modules of the proposed method use the same back-
-bone architecture, which is an hourglass network with skip
-connections between the contracting and expanding parts,
-similar to [10]. The backbone architecture is described
-in more detail in the supplementary materials. Regarding
-the learning representation [7] used to encode the event
-sequences, all modules use the voxel grid representation.
-Specifically, for event sequence E0!endwe compute a
-voxel gridV0!endfollowing the procedure described
-in [46]. In the following paragraphs, we analyze each mod-
-ule and its scope within the overall framework.
-Interpolation by synthesis , as shown in Fig. 3b, directly
-regresses a new frame ^Isyngiven the left I0and rightI1
-RGB keyframes and events sequences E0!andE!1re-
-spectively. The merits of this interpolation scheme lie in
-its ability to handle changes in lighting, such as water re-
-flections in Fig. 6 and a sudden appearance of new objects
-in the scene, because unlike warping-based method, it does
-not rely on the brightness constancy assumption. Its main
-drawback is the distortion of image edges and textures when
-event information is noisy or insufficient because of high
-contrast thresholds, e.g. triggered by the book in Fig. 6.
-Warping-based interpolation , shown in Fig. 3d, first
-estimates the optical flow F!0andF!1between a la-
-tent new frame ^Iand boundary keyframes I0andI1using
-eventsE!0andE!1respectively. We compute E!0,
-by reversing the event sequence E0!, as shown in Fig. 4.
-Then our method uses computed optical flow to warp the
-boundary keyframes in timestep using differentiable in-
-terpolation [9], which in turn produces two new frame esti-
-mates ^Iwarp
-0!and^Iwarp
-1!.
-The major difference of our approach from the tradi-
-tional warping-based interpolation methods [20, 10, 21, 43],
-is that the latter compute optical flow between keyframes
-using the frames themselves and then approximate opti-
-cal flow between the latent middle frame and boundary
-by using a linear motion assumption. This approach does
-not work when motion between frames is non-linear and
-keyframes suffer from motion blur. By contrast, our ap-
-proach computes the optical flow from the events, and thus
-can naturally handle blur and non-linear motion. Although
-events are sparse, the resulting flow is sufficiently dense as
-shown in Fig. 3d, especially in textured areas with dominant
-mostion, which is most important for interpolation.
-Moreover, the warping-based interpolation approach re-
-lying on events also works better than synthesis-based
-method in the scenarios when event data is noisy or not(a) Overview of the proposed method.
- (b) Interpolation by synthesis module.
-(c) Attention-based averaging module.
-(d) Warping-based interpolation module.
-(e) Warping refinement module.
-Figure 3: Structure of the proposed method. The overall workflow of the method is shown in Fig. 3a and individual modules
-are shown in Fig. 3d, 3b, 3e and 3c. In the figures we also show loss function that we use to train each module. We show
-similar modules in the same color across the figures.
-Figure 4: Example of an event sequence reversal.
-sufficient due to high contrast thresholds, e.g. the book in
-Fig. 6. On the down side, this method still relies on the
-brightness constancy assumption for optical flow estimation
-and thus can not handle brightness changes and new ob-
-jects appearing between keyframes, e.g. water reflections
-in Fig. 6.
-Warping refinement module computes refined interpo-
-lated frames, ^Irefine
-0!and^Irefine
-1!, by estimating residual op-
-tical flow, 
F!0and
F!1respectively, between the
-warping-based interpolation results, ^Iwarp
-0!and^Iwarp
-1!, and
-the synthesis result ^Isyn
-. It then proceeds by warping ^Iwarp
-0!
-and^Iwarp
-1!for a second time using the estimated residual
-optical flow, as shown in Fig. 3e. The refinement module
-draws inspiration from the success of optical flow and dis-
-parity refinement modules in [8, 27], and also by our ob-
-servation that the synthesis interpolation results are usually
-perfectly aligned with the ground-truth new frame. Besides
-computing residual flow, the warping refinement module
-also performs inpainting of the occluded areas, by fillingthem with values from nearby regions.
-Finally, the attention averaging module, shown in
-Fig. 3c, blends in a pixel-wise manner the results of synthe-
-sis^Isyn
-and warping-based interpolation ^Irefine
-0!and^Irefine
-1!to
-achieve final interpolation result ^I. This module leverages
-the complementarity of the warping- and synthesis-based
-interpolation methods and produces a final result, which is
-better than the results of both methods by 1.73 dB in PSNR
-as shown in Tab. 1 and illustrated in Fig. 6.
-A similar strategy was used in [21, 10], however these
-works only blended the warping-based interpolation results
-to fill the occluded regions, while we blend both warping
-and synthesis-based results, and thus can also handle light
-changes. We estimate the blending coefficients using an at-
-tention network that takes as an input the interpolation re-
-sults, ^Irefine
-0!,^Irefine
-1!and^Isyn, the optical flow results F!0
-andF!1and bi-linear coefficient , that depends on the
-position of the new frame as a channel with constant value.
-2.1. High Speed Events-RGB (HS-ERGB) dataset
-Due to the lack of available datasets that combine
-synchronized, high-resolution event cameras and standard
-RGB cameras, we build a hardware synchronized hybrid
-sensor which combines a high-resolution event camera withEvent Camera
-Prophesee Gen4M 720p
-Resolution 1280   720 
-RGB Camera
-FLIR BlackFly S 
-Resolution: 1440   1080
-2.5 cm baselineFigure 5: Illustration of the dual camera setup. It comprises
-a Prophesee Gen4 720p monochrome event camera (top)
-and a FLIR BlackFly S RGB camera (bottom). Both cam-
-eras are hardware synchronized with a baseline of 2:5 cm
-.
-a high resolution and high-speed color camera. We use this
-hybrid sensor to record a new large-scale dataset which we
-term the High-Speed Events and RGB (HS-ERGB) dataset
-which we use to validate our video frame interpolation ap-
-proach. The hybrid camera setup is illustrated in Fig. 5.
-It features a Prophesee Gen4 (1280 720) event camera
-(Fig. 5 top) and a FLIR BlackFly S global shutter RGB cam-
-era (1440 1080) (Fig. 5 bottom), separated by a baseline
-of2:5 cm . Both cameras are hardware synchronized and
-share a similar field of view (FoV). We provide a detailed
-comparison of our setup against the commercially available
-DA VIS 346 [4] and the recently introduced setup [40] in
-the appendix.Compared to both [4] and [40] our setup is
-able to record events at much higher resolution (1280 720
-vs. 240 180 or 346 260) and standard frames at much
-higher framerate (225 FPS vs. 40 FPS or 35 FPS) and with
-a higher dynamic range (71.45 dB vs. 55 dB or 60 dB).
-Moreover, standard frames have a higher resolution com-
-pared to the DA VIS sensor (1440 1080 vs. 240 180) and
-provide color. The higher dynamic range and frame rate,
-enable us to more accurately compare event cameras with
-standard cameras in highly dynamic scenarios and high dy-
-namic range. Both cameras are hardware synchronized and
-aligned via rectification and global alignment. For more
-synchronization and alignment details see the appendix.
-We record data in a variety of conditions, both indoors
-and outdoors. Sequences were recorded outdoors with ex-
-posure times as low as 100µsor indoors with exposure
-times up to 1000 µs. The dataset features frame rates of
-160 FPS, which is much higher than previous datasets, en-
-abling larger frame skips with ground truth color frames.
-The dataset includes highly dynamic close scenes with non-
-linear motions and far-away scenes featuring mainly cam-
-era ego-motion. For far-away scenes, stereo rectification is
-sufficient for good per-pixel alignment. For each sequence,
-alignment is performed depending on the depth either by
-stereo rectification or using feature-based homography esti-
-mation.To this end, we perform standard stereo calibration
-between RGB images and E2VID [32] reconstructions and
-rectify the images and events accordingly. For the dynamic
-close scenes, we additionally estimate a global homogra-
-phy by matching SIFT features [17] between these two im-ages. Note that for feature-based alignment to work well,
-the camera must be static and objects of interest should only
-move in a fronto-parallel plane at a predetermined depth.
-While recording we made sure to follow these constraints.
-For a more detailed dataset overview we refer to the sup-
-plementary material.
-3. Experiments
-All experiments in this work are done using the Py-
-Torch framework [30]. For training, we use the Adam
-optimizer[12] with standard settings, batches of size 4 and
-learning rate 104, which we decrease by a factor of 10ev-
-ery 12 epoch. We train each module for 27 epoch. For the
-training, we use large dataset with synthetic events gener-
-ated from Vimeo90k septuplet dataset [44] using the video to
-events method [6], based on the event simulator from [31].
-We train the network by adding and training modules
-one by one, while freezing the weights of all previously
-trained modules. We train modules in the following or-
-der: synthesis-based interpolation, warping-based interpo-
-lation, warping refinement, and attention averaging mod-
-ules. We adopted this training because end-to-end training
-from scratch does not converge, and fine-tuning of the en-
-tire network after pretraining only marginally improved the
-results. We supervise our network with perceptual [45] and
-L1losses as shown in Fig. 3b, 3d, 3e and 3c. We fine-tune
-our network on real data module-by-module in the order
-of training. To measure the quality of interpolated images
-we use structural similarity (SSIM) [39] and peak signal to
-noise ratio (PSNR) metrics.
-Note, that the computational complexity of our interpo-
-lation method is among the best: on our machine for image
-resolutions of 640480, a single interpolation on the GPU
-takes 878 ms for DAIN [3], 404 ms for BMBC [29], 138 ms
-for ours, 84 ms for RRIN [13], 73 ms for Super SloMo [10]
-and 33 ms for LEDVDI [15] methods.
-3.1. Ablation study
-To study the contribution of every module of the pro-
-posed method to the final interpolation, we investigate the
-interpolation quality after each module in Fig. 3a, and re-
-port their results in Tab. 1. The table shows two notable re-
-sults. First, it shows that adding a warping refinement block
-after the simple warping block significantly improves the
-interpolation result. Second, it shows that by attention aver-
-aging synthesis-based and warping-based results, the inter-
-polations are improved by 1.7 dB in terms of PSNR. This
-is because the attention averaging module combines the ad-
-vantages of both methods. To highlight this further, we il-
-lustrate example reconstructions from these two modules in
-Fig. 6. As can be seen, the warping-based module excels at
-reconstructing textures in non-occluded areas (fourth col-
-umn) while the synthesis module performs better in regionswith difficult lighting conditions (fifth column). The atten-
-tion module successfully combines the best parts of both
-modules (first column).
-Figure 6: Complementarity of warping- and synthesis-
-based interpolation.
-Table 1: Quality of interpolation after each module on
-Vimeo90k (denoising) validation set. For SSIM and PSNR
-we show mean and one standard deviation. The best result
-is highlighted.
-Module PSNR SSIM
-Warping interpolation 26.68 3.68 0.926 0.041
-Interpolation by synthesis 34.10 3.98 0.964 0.029
-Warping refinement 33.02 3.76 0.963 0.026
-Attention averaging (ours) 35.83 3.70 0.976 0.019
-3.2. Benchmarking
-Synthetic datasets. We compare the proposed
-method, which we call Time Lens , to four state-of-the-art
-frame-based interpolation methods DAIN [3],RRIN [13],
-BMBC [29], SuperSloMo [10], event-based video recon-
-struction method E2VID [33] and two event and frame-
-based methods EDI [26] and LEDVDI [15] on pop-
-ular video interpolation benchmark datasets, such as
-Vimeo90k (interpolation) [44], Middlebury [2]. During
-the evaluation, we take original video sequence, skip 1
-or 3 frames respectively, reconstruct them using interpola-
-tion method and compare to ground truth skipped frames.
-Events for event-based methods we simulate using [6] from
-the skipped frames. We do not fine-tune the methods for
-each dataset but simply use pre-trained models provided by
-the authors. We summarise the results in Tab. 2.
-As we can see, the proposed method outperforms other
-method across datasets in terms of average PSNR (up to
-8.82 dB improvement) and SSIM scores (up to 0.192 im-
-provement). As before these improvements stem from the
-use of auxiliary events during the prediction stage which
-allow our method to perform accurate frame interpolation,
-event for very large non-linear motions. Also, it has signif-
-icantly lower standard deviation of the PSNR (2.53 dB vs.
-4.96 dB) and SSIM (0.025 vs. 0.112) scores, which sug-
-gests more consistent performance across examples. Also,we can see that PSNR and SSIM scores of the proposed
-method degrades to much lesser degree than scores of the
-frame-based methods (up to 1.6 dB vs. up to 5.4 dB), as we
-skip and attempt to reconstruct more frames. This suggests
-that our method is more robust to non-linear motion than
-frame-based methods.
-High Quality Frames (HQF) dataset. We also evalu-
-ate our method on High Quality Frames (HQF) dataset [35]
-collected using DA VIS240 event camera that consists of
-video sequences without blur and saturation. During eval-
-uation, we use the same methodology as for the synthetic
-datasets, with the only difference that in this case we use
-real events. In the evaluation, we consider two versions of
-our method: Time Lens-syn , which we trained only on syn-
-thetic data, and Time Lens-real , which we trained on syn-
-thetic data and fine-tuned on real event data from our own
-DA VIS346 camera. We summarise our results in Tab. 3.
-The results on the dataset are consistent with the re-
-sults on the synthetic datasets: the proposed method outper-
-forms state-of-the-art frame-based methods and produces
-more consistent results over examples. As we increase the
-number of frames that we skip, the performance gap be-
-tween the proposed method and the other methods widens
-from 2.53 dB to 4.25 dB, also the results of other methods
-become less consistent which is reflected in higher devia-
-tion of PSNR and SSIM scores. For a more detailed dis-
-cussion about the impact of frame skip length and perfor-
-mance, see the appendix. Interestingly, fine-tuning of the
-proposed method on real event data, captured by another
-camera, greatly boosts the performance of our method by
-an average of 1.94 dB. This suggest that existence of large
-domain gap between synthetic and real event data.
-High Speed Event-RGB dataset. Finally, we evaluate
-our method on our dataset introduced in § 2.1. As clear from
-Tab. 4, our method, again significantly outperforms frame-
-based and frame-plus-event-based competitors. In Fig. 7 we
-show several examples from the HS-ERGB test set which
-show that, compared to competing frame-based method,
-our method can interpolate frames in the case of nonlin-
-ear (“Umbrella” sequence) and non-rigid motion (“Water
-Bomb”), and also handle illumination changes (“Fountain
-Schaffhauserplatz” and “Fountain Bellevue”).
-4. Conclusion
-In this work, we introduce Time Lens, a method that
-can show us what happens in the blind-time between
-two intensity frames using high temporal resolution in-
-formation from an event camera. It works by leveraging
-the advantages of synthesis-based approaches, which can
-handle changing illumination conditions and non-rigid
-motions, and flow-based approach, relying on motion
-estimation from events. It is therefore robust to motion blur
-and non-linear motions. The proposed method achievesTable 2: Results on standard video interpolation benchmarks such as Middlebury [2],Vimeo90k (interpolation) [44] and
-GoPro [19]. In all cases, we use a test subset of the datasets. To compute SSIM and PSNR, we downsample the original
-video and reconstruct the skipped frames. For Middlebury and Vimeo90k (interpolation), we skip 1 and 3 frames, and for
-GoPro we skip 7 and 15 frames due its its high frame rate of 240 FPS. Uses frames andUses events indicate if a method uses
-frames and events for interpolation. For event-based methods we generate events from the skipped frames using the event
-simulator [6]. Color indicates if a method works with color frames. For SSIM and PSNR we show mean and one standard
-deviation. Note, that we can not produce results with 3 skips on the Vimeo90k dataset, since it consists of frame triplet. We
-show the best result in each column in bold and the second-best using underscore text.
-Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
-Middlebury [2] 1 frame skip 3 frames skips
-DAIN [3] 4 8 4 30.875.38 0.899 0.110 26.674.53 0.838 0.130
-SuperSloMo [10] 4 8 4 29.755.35 0.880 0.112 26.43 5.30 0.823 0.141
-RRIN [13] 4 8 4 31.085.55 0.8960.112 27.18 5.57 0.8370.142
-BMBC [29] 4 8 4 30.836.01 0.897 0.111 26.86 5.82 0.834 0.144
-E2VID [32] 8 4 8 11.262.82 0.427 0.184 26.86 5.82 0.834 0.144
-EDI [26] 4 4 8 19.722.95 0.725 0.155 18.44 2.52 0.669 0.173
-Time Lens (ours) 4 4 4 33.273.11 0.929 0.027 32.13 2.81 0.908 0.039
-Vimeo90k (interpolation) [44] 1 frame skip 3 frames skips
-DAIN [3] 4 8 4 34.204.43 0.962 0.023 - -
-SuperSloMo [10] 4 8 4 32.934.23 0.948 0.035 - -
-RRIN [13] 4 8 4 34.724.40 0.9620.029 - -
-BMBC [29] 4 8 4 34.564.40 0.962 0.024 - -
-E2VID [32] 8 4 8 10.082.89 0.395 0.141 - -
-EDI [26] 4 4 8 20.743.31 0.748 0.140 - -
-Time Lens (ours) 4 4 4 36.313.11 0.962 0.024 - -
-GoPro [19] 7 frames skip 15 frames skips
-DAIN [3] 4 8 4 28.814.20 0.876 0.117 24.39 4.69 0.7360.173
-SuperSloMo [10] 4 8 4 28.984.30 0.875 0.118 24.38 4.78 0.747 0.177
-RRIN [13] 4 8 4 28.964.38 0.876 0.119 24.324.80 0.749 0.175
-BMBC [29] 4 8 4 29.084.58 0.8750.120 23.68 4.69 0.736 0.174
-E2VID [32] 8 4 8 9.742.11 0.549 0.094 9.75 2.11 0.549 0.094
-EDI [26] 4 4 8 18.792.03 0.670 0.144 17.45 2.23 0.603 0.149
-Time Lens (ours) 4 4 4 34.811.63 0.959 0.012 33.21 2.00 0.942 0.023
-Table 3: Benchmarking on the High Quality Frames (HQF) DA VIS240 dataset. We do not fine-tune our method and other
-methods and use models provided by the authors. We evaluate methods on all sequences of the dataset. To compute SSIM
-and PSNR, we downsample the original video by skip 1 and 3 frames, reconstruct these frames and compare them to the
-skipped frames. In Uses frames andUses events columns we specify if a method uses frames and events for interpolation.
-In the Color column, we indicate if a method works with color frames. In the table, we present two versions of our method:
-Time Lens-syn , which we trained only on synthetic data, and Time Lens-real , which we trained on synthetic data and fine-
-tuned on real event data from our own DA VIS346 camera. For SSIM and PSNR, we show mean and one standard deviation.
-We show the best result in each column in bold and the second-best using underscore text.
-Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
-1 frame skip 3 frames skips
-DAIN [3] 4 8 4 29.826.91 0.875 0.124 26.10 7.52 0.782 0.185
-SuperSloMo [10] 4 8 4 28.766.13 0.861 0.132 25.54 7.13 0.761 0.204
-RRIN [13] 4 8 4 29.767.15 0.874 0.132 26.11 7.84 0.778 0.200
-BMBC [29] 4 8 4 29.967.00 0.8750.126 26.327.78 0.7810.193
-E2VID [32] 8 4 8 6.702.19 0.315 0.124 6.70 2.20 0.315 0.124
-EDI [26] 4 4 8 18.76.53 0.574 0.244 18.8 6.88 0.579 0.274
-Time Lens-syn (our) 4 4 4 30.575.01 0.903 0.067 28.98 5.09 0.873 0.086
-Time Lens-real (ours) 4 4 4 32.494.60 0.927 0.048 30.57 5.08 0.900 0.069Figure 7: Qualitative results for the proposed method and its closes competitor DAIN [3] on our Dual Event and Color Camera
-Dataset test sequences: “Fountain Schaffhauserplatz” (top-left), “Fountain Bellevue” (bottom-left) “Water bomb” (top-right)
-and “Umbrella” (bottom-right). For each sequence, the figure shows interpolation results on the left (the animation can be
-viewed in Acrobat Reader) and close-up interpolation results on the right. The close-ups, show input left and right frame and
-intermediate interpolated frames.
-Table 4: Benchmarking on the test set of the High Speed Event and RGB camera (HS-ERGB) dataset. We report PSNR and
-SSIM for all sequences by skipping 5 and 7 frames respectively, and reconstructing the missing frames with each method. By
-design LEDVDI [15] can interpolate only 5 frames. Uses frames andUses events indicate if a method uses frames or events
-respectively. Color indicates whether a method works with color frames. For SSIM and PSNR the scores are averaged over
-the sequences. Best results are shown in bold and the second best are underlined.
-Method Uses frames Uses events Color PSNR SSIM PSNR SSIM
-Far-away sequences 5 frame skip 7 frames skips
-DAIN [3] 4 8 4 27.921.55 0.7800.141 27.131.75 0.7480.151
-SuperSloMo [10] 4 8 4 25.666.24 0.727 0.221 24.16 5.20 0.692 0.199
-RRIN [13] 4 8 4 25.265.81 0.738 0.196 23.73 4.74 0.703 0.170
-BMBC [29] 4 8 4 25.626.13 0.742 0.202 24.13 4.99 0.710 0.175
-LEDVDI [15] 4 4 8 12.501.74 0.393 0.174 n/a n/a
-Time Lens (ours) 4 4 4 33.132.10 0.877 0.092 32.31 2.27 0.869 0.110
-Close planar sequences 5 frame skip 7 frames skips
-DAIN [3] 4 8 4 29.034.47 0.807 0.093 28.50 4.54 0:8010:096
-SuperSloMo [10] 4 8 4 28.354.26 0.788 0.098 27.27 4.26 0:7750:099
-RRIN [13] 4 8 4 28.694.17 0.813 0.083 27.46 4.24 0.800 0.084
-BMBC [29] 4 8 4 29.224.45 0.8200.085 27.994.55 0.808 0.084
-LEDVDI [15] 4 4 8 19.464.09 0.602 0.164 n/a n/a
-Time Lens (ours) 4 4 4 32.194.19 0.839 0.090 31.68 4.18 0.835 0.091
-an up to 5.21 dB improvement over state-of-the-art
-frame-based and event-plus-frames-based methods on both
-synthetic and real datasets. In addition, we release the
-first High Speed Event and RGB (HS-ERGB) dataset,
-which aims at pushing the limits of existing interpola-
-tion approaches by establishing a new benchmark for both
-event- and frame-based video frame interpolation methods.5. Acknowledgement
-This work was supported by Huawei Zurich Research
-Center; by the National Centre of Competence in Re-
-search (NCCR) Robotics through the Swiss National Sci-
-ence Foundation (SNSF); the European Research Coun-
-cil (ERC) under the European Union’s Horizon 2020 re-
-search and innovation programme (Grant agreement No.
-864042).6. Video Demonstration
-This PDF is accompanied with a video showing advan-
-tages of the proposed method compared to state-of-the-art
-frame-based methods published over recent months, as well
-as potential practical applications of the method.
-7. Backbone network architecture
-Figure 8: Backbone hourglass network that we use in all
-modules of the proposed method.
-For all modules in the proposed method, we use the same
-backbone architecture which is an hourglass network with
-shortcut connections between the contracting and the ex-
-panding parts similar to [10] which we show in Fig. 8.
-8. Additional Ablation Experiments
-0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35
-Percentage of locationsSynthesisWarped successiveWarp preceeding
-Figure 9: Percentage of pixels each interpolation method
-contributes on average to the final interpolation result for
-Vimeo90k (denoising) validation set. Note, that all meth-
-ods contribute almost equally to the final result and thus are
-equally important.
-Table 5: Importance of inter-frame events on Middlebury
-test set. To compute SSIM and PSNR, we skip one frame
-of the original video, reconstruct it and compare to the
-skipped frame. One version of the proposed method has
-access to the events synthesized from the skipped frame
-and another version does not have inter-frame information.
-We also show performance of frame-based SuperSloMo
-method [10], that is used in event simulator for reference.
-We highlight the best performing method.
-Method PSNR SSIM
-With inter-frame events (ours) 33.27 3.11 0.929 0.027
-Without inter-frame events 29.03 4.85 0.866 0.111
-SuperSloMo [10] 29.75 5.35 0.880 0.112
-Importance of inter-frame events . To study the im-
-portance of additional information provided by events, we
-skip every second frame of the original video and attempt
-to reconstruct it using two versions of the proposed method.One version has access to the events synthesized from the
-skipped frame and another version does not have inter-
-frame information. As we can see from the Tab. 5, the
-former significantly outperforms the later by a margin of
-4.24dB. Indeed this large improvements can be explained
-by the fact that the method with inter-frame events has im-
-plicit access to the ground truth image it tries to recon-
-struct, albeit in the form of asynchronous events. This high-
-lights that our network is able to efficiently decode the asyn-
-chronous intermediate events to recover the missing frame.
-Moreover, this shows that the addition of events has a sig-
-nificant impact on the final task performance, proving the
-usefulness of an event camera as an auxiliary sensor.
-Importance of each interpolation method. To study
-relative importance of synthesis-based andwarping-based
-interpolation methods, we compute the percentage of pixels
-that each method contribute on average to the final interpo-
-lation result for the Vimeo90k (denoising) validation dataset
-and show the result in Fig. 9. As it is clear from the figure,
-all the methods contribute almost equally to the final result
-and thus are all equally important.
-1 2 3 4 5 6 7
-Frame Index2224262830PSNR [dB]
-BMBC
-DAINRRIN
-SuperSlowMoOurs
-Figure 10: “Rope plot” showing interpolation quality as a
-function of distance from input boundary frames on High
-Quality Frames dataset. We skip all but every 7th frame and
-restore them using events and remaining frames. For each
-skip position, we compute average PSNR of the restored
-frame over entire dataset. We do not fine-tune the proposed
-and competing methods on the HQF dataset and simply use
-pre-trained models provided by the authors. Note, that the
-proposed method have the highest PSNR. Also, its PSNR
-decreases much slower than PSNR of other methods we
-move away from the input boundary frames.
-“Rope” plot. To study how the interpolation quality de-
-creases with the distance to the input frames, we skip all but
-every 7th frame in the input videos from the High Quality
-Frames dataset, restore them using our method and compare
-to the original frames. For each skipped frame position, we
-compute average PSNR of the restored frame over entire
-dataset and show results in Fig. 10. As clear from the fig-ure, the proposed method has the highest PSNR. Also, its
-PSNR decreases much slower than PSNR of the competing
-methods as we move away from the boundary frames.
-9. Additional Benchmarking Results
-To makes sure that the fine-tuning does not af-
-fect our general conclusions, we fine-tuned the pro-
-posed method and RRIN method [13] on subset of
-High Quality Frames dataset and test them on the
-remaining part (“poster pillar 1”, “slow andfastdesk”,
-“bike bayhdr” and “desk” sequences). We choose RRIN
-method for this experiment, because it showed good perfor-
-mance across synthetic and real datasets and it is fairly sim-
-ple. As clear from the Tab. 6, after the fine-tuning, perfor-
-mance of the proposed method remained very strong com-
-pared to the RRIN method.
-10. High Speed Events and RGB Dataset
-In this section we describe the sequences in the High-
-Speed Event and RGB (HS-ERGB) dataset. The commer-
-cially available DA VIS 346 [4] already allows the simul-
-taneous recording of events and grayscale frames, which
-are temporally and spatially synchronized. However, it has
-some shortcomings as the relatively low resolution of only
-346260 pixels of both frames and events. This is far
-below the resolution of typical frame based consumer cam-
-eras. Additionally, the DA VIS 346 has a very limited dy-
-namic range of 55 db and a maximum frame of 40 FPS.
-Those properties render it not ideal for many event based
-methods, which aim to outperform traditional frame based
-cameras in certain applications. The setup described in [40]
-shows improvements in the resolution of frames and dy-
-namic range, but has a reduced event resolution instead. The
-lack of publicly available high resolution event and color
-frame datasets and of the shelf hardware motivated the de-
-velopment of our dual camera setup. It features high reso-
-lution, high frame rate, high dynamic range color frames
-combined with high resolution events. A comparison of
-our setup with the DA VIS346[4] and the setup with beam
-splitter in [40] is shown in 7. With this new setup we col-
-lect new High Speed Events and RGB (HS-ERGB) Dataset
-that we summarize in Tab. 8. We show several fragments
-from the dataset in Fig. 12. In the following paragraphs we
-describe temporal synchronization and spatial alignment of
-frame and event data that we performed for our dataset.
-Synchronization In our setup, two cameras are hard-
-ware synchronized through the use of external triggers.
-Each time the standard camera starts and ends exposure, a
-trigger is sent to the event camera which records an exter-
-nal trigger event with precise timestamp information. This
-information allows us to assign accurate timestamps to the
-standard frames, as well as group events during exposure orbetween consecutive frames.
-Alignment In our setup event and RGB cameras are ar-
-ranged in stereo configuration, therefore event and frame
-data in addition to temporal, require spatial alignment. We
-perform the alignment in three steps: (i)stereo calibration,
-(ii)rectification and (iii) feature-based global alignment.
-We first calibrate the cameras using a standard checker-
-board pattern. The recorded asynchronous events are con-
-verted to temporally aligned video reconstructions using
-E2VID[32, 33]. Finally, we find the intrinsic and extrin-
-sics by applying the stereo calibration tool Kalibr[24] to the
-video reconstructions and the standard frames recorded by
-the color camera. We then use the found intrinsics and ex-
-trinsics to rectify the events and frames.
-Due to the small baseline and similar fields of view
-(FoV), stereo rectification is usually sufficient to align the
-output of both sensors for scenes with a large average depth
-(>40 m ). This is illustrated in Fig. 11 (a).
-For close scenes, however, events and frames are mis-
-aligned (Fig. 11 (b)). For this reason we perform the sec-
-ond step of global alignment using a homography which
-we estimate by matching SIFT features [17] extracted on
-the standard frames and video reconstructions. The homog-
-raphy estimation also utilizes RANSAC to eliminate false
-matches. When the cameras are static, and the objects of
-interest move within a plane, this yields accurate alignment
-between the two sensors (Fig.11 (c)).
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-Sequence Name Subset Camera Settings Description
-Close planar sequences
-Water bomb air (Fig. 12a)
-Train163 FPS, 1080 µsexposure, 1065 frames accelerating object, water splash
-Lighting match 150 FPS, 2972 µsexposure, 666 frames illumination change, fire
-Fountain Schaffhauserplatz 1 150 FPS, 977µsexposure, 1038 frames illumination change, fire
-Water bomb ETH 2 (Fig. 12c) 163 FPS, 323µsexposure, 3494 frames accelerating object, water splash
-Waving arms 163 FPS, 3476 µsexposure, 762 frames non-linear motion
-Popping air balloon
-Test150 FPS, 2972 µsexposure, 335 frames non-linear motion, object disappearance
-Confetti (Fig. 12e 150 FPS, 2972 µsexposure, 832 frames non-linear motion, periodic motion
-Spinning plate 150 FPS, 2971 µsexposure, 1789 frames non-linear motion, periodic motion
-Spinning umbrella 163 FPS, 3479 µsexposure, 763 frames non-linear motion
-Water bomb floor 1 (Fig. 12d) 160 FPS, 628µsexposure, 686 frames accelerating object, water splash
-Fountain Schaffhauserplatz 2 150 FPS, 977µsexposure, 1205 frames non-linear motion, water
-Fountain Bellevue 2 (Fig. 12b) 160 FPS, 480µsexposure, 1329 frames non-linear motion, water, periodic movement
-Water bomb ETH 1 163 FPS, 323µsexposure, 3700 frames accelerating object, water splash
-Candle (Fig. 12f) 160 FPS, 478µsexposure, 804 frames illumination change, non-linear motion
-Far-away sequences
-Kornhausbruecke letten x 1
-Train163 FPS, 266µsexposure, 831 frames fast camera rotation around z-axis
-Kornhausbruecke rot x 5 163 FPS, 266µsexposure, 834 frames fast camera rotation around x-axis
-Kornhausbruecke rot x 6 163 FPS, 266µsexposure, 834 frames fast camera rotation around x-axis
-Kornhausbruecke rot y 3 163 FPS, 266µsexposure, 833 frames fast camera rotation around y-axis
-Kornhausbruecke rot y 4 163 FPS, 266µsexposure, 833 frames fast camera rotation around y-axis
-Kornhausbruecke rot z 1 163 FPS, 266µsexposure, 857 frames fast camera rotation around z-axis
-Kornhausbruecke rot z 2 163 FPS, 266µsexposure, 833 frames fast camera rotation around z-axis
-Sihl 4 163 FPS, 426µsexposure, 833 frames fast camera rotation around z-axis
-Tree 3 163 FPS, 978µsexposure, 832 frames camera rotation around z-axis
-Lake 4 163 FPS, 334µsexposure, 833 frames camera rotation around z-axis
-Lake 5 163 FPS, 275µsexposure, 833 frames camera rotation around z-axis
-Lake 7 163 FPS, 274µsexposure, 833 frames camera rotation around z-axis
-Lake 8 163 FPS, 274µsexposure, 832 frames camera rotation around z-axis
-Lake 9 163 FPS, 274µsexposure, 832 frames camera rotation around z-axis
-Bridge lake 4 163 FPS, 236µsexposure, 836 frames camera rotation around z-axis
-Bridge lake 5 163 FPS, 236µsexposure, 834 frames camera rotation around z-axis
-Bridge lake 6 163 FPS, 235µsexposure, 832 frames camera rotation around z-axis
-Bridge lake 7 163 FPS, 235µsexposure, 832 frames camera rotation around z-axis
-Bridge lake 8 163 FPS, 235µsexposure, 834 frames camera rotation around z-axis
-Kornhausbruecke letten random 4
-Test163 FPS, 266µsexposure, 834 frames random camera movement
-Sihl 03 163 FPS, 426µsexposure, 834 frames camera rotation around z-axis
-Lake 01 163 FPS, 335µsexposure, 784 frames camera rotation around z-axis
-Lake 03 163 FPS, 334µsexposure, 833 frames camera rotation around z-axis
-Bridge lake 1 163 FPS, 237µsexposure, 833 frames camera rotation around z-axis
-Bridge lake 3 163 FPS, 236µsexposure, 834 frames camera rotation around z-axis(a) Water bomb air (b) Fountain Bellevue
-(c) Water bomb ETH 2 (d) Water bomb floor 1
-(e) Confetti (f) Candle
-Figure 12: Example sequences of the HS-ERGB dataset. This figure contains animation that can be viewed in Acrobat
-Reader.

txt/2106.08858.txt

@@ -1,901 +0,0 @@
-Grounding Spatio-Temporal Language with
-Transformers
-Tristan Karch, Laetitia Teodorescu
-Inria - Flowers Team
-Université de Bordeaux
-[email protected] Hofmann
-Microsoft Research
-Cambridge, UKClément Moulin-Frier
-Inria - Flowers team
-Université de Bordeaux
-ENSTA ParisTech
-Pierre-Yves Oudeyer
-Inria - Flowers team
-Université de Bordeaux
-ENSTA ParisTech
-Abstract
-Language is an interface to the outside world. In order for embodied agents to use
-it, language must be grounded in other, sensorimotor modalities. While there is
-an extended literature studying how machines can learn grounded language, the
-topic of how to learn spatio-temporal linguistic concepts is still largely uncharted.
-To make progress in this direction, we here introduce a novel spatio-temporal
-language grounding task where the goal is to learn the meaning of spatio-temporal
-descriptions of behavioral traces of an embodied agent. This is achieved by
-training a truth function that predicts if a description matches a given history of
-observations. The descriptions involve time-extended predicates in past and present
-tense as well as spatio-temporal references to objects in the scene. To study the role
-of architectural biases in this task, we train several models including multimodal
-Transformer architectures; the latter implement different attention computations
-between words and objects across space and time. We test models on two classes of
-generalization: 1) generalization to randomly held-out sentences; 2) generalization
-to grammar primitives. We observe that maintaining object identity in the attention
-computation of our Transformers is instrumental to achieving good performance
-on generalization overall, and that summarizing object traces in a single token has
-little influence on performance. We then discuss how this opens new perspectives
-for language-guided autonomous embodied agents. We also release our code under
-open-source license as well as pretrained models and datasets to encourage the
-wider community to build upon and extend our work in the future.
-1 Introduction
-Building autonomous agents that learn to represent and use language is a long standing-goal in
-Artificial Intelligence [20,39]. In developmental robotics [ 7], language is considered a cornerstone
-of development that enables embodied cognitive agents to learn more efficiently through social
-interactions with humans or through cooperation with other autonomous agents. It is also considered
-essential to develop complex sensorimotor skills, facilitating the representation of behaviors and
-actions.
-Embodied Language Grounding [50] is the field that studies how agents can align language with
-their behaviors in order to extract the meaning of linguistic constructions. Early approaches in
-Equal Contribution
-35th Conference on Neural Information Processing Systems (NeurIPS 2021).arXiv:2106.08858v2  [cs.AI]  11 Oct 2021shake
-grow
-- ‘Shake red cat’
-- ‘Shake thing right of lamp’
-- ‘Shake thing was left of lamp ’- ‘Was grasp blue cactus’
-- ‘Was grasp thing top most’
-- ‘Was grasp thing was right of parrot’(a) Grow action with spatial  or
-attribute reference to object(b) Shake action with  spatio-temporal  
-reference to object (c) Grasp past actions  description with
- spatio-temporal reference to object
-- ‘Grow blue chameleon’
-- ‘Grow thing left of tv ’
-- ‘Grow thing left most ’
-- ‘Grasp green water ’
-Traces of interactions Sampled 
-descriptionsNatural
-English 
-version ‘You are currently growing the 
-blue chameleon’‘You are currently shaking some -
-thing that used to be at the left of 
-the lamp’‘You previously grasped some-
-thing that used to be at the right 
-of the parrot’Figure 1: Visual summary of the Temporal Playground environment: At each episode (column
-a, b and c), the actions of an agent (represented by a hand) unfold in the environment and generate
-a trace of interactions between objects and the agent body. Given such a trace, the environment
-automatically generates a set of synthetic linguistic descriptions that are true at the end of the trace.
-In (a) the agent grows an object which is described with spatial (underlined) or attribute (highlighted)
-reference. In (b) it shakes an object which is described with attribute, spatial or spatio-temporal
-(underlined) reference. In (c) it has grasped an object (past action underlined) which is described
-with attribute, spatial or spatio-temporal (highlighted) reference. The natural english version is given
-for illustrative purposes.
-developmental robotics studied how various machine learning techniques, ranging from neural
-networks [ 40,45,24] to non-negative matrix factorization [ 32], could enable the acquisition of
-grounded compositional language [ 42,41]. This line of work was recently extended using techniques
-forLanguage conditioned Deep Reinforcement Learning [31]. Among these works we can distinguish
-mainly three language grounding strategies. The first one consists of directly grounding language
-in the behavior of agents by training goal-conditioned policies satisfying linguistic instructions
-[40,45,23,22,9]. The second aims at extracting the meaning of sentences from mental simulations
-(i.e. generative models) of possible sensorimotor configurations matching linguistic descriptions
-[32,1,12,34]. The third strategy searches to learn the meaning of linguistic constructs in terms of
-outcomes that agents can observe in the environment. This is achieved by training a truth function that
-detects if descriptions provided by an expert match certain world configurations. This truth function
-can be obtained via Inverse Reinforcement Learning [49,3] or by training a multi-modal binary
-classifier [ 14]. Previous work [ 14,3] has shown that access to this reward is enough for sucessfully
-grounding language in instruction-following agents.
-While all the above-mentioned approaches consider language that describes immediate and instanta-
-neous actions, we argue that it is also important for agents to grasp language expressing concepts
-that are relational and that span multiple time steps. We thus propose to study the grounding of new
-spatio-temporal concepts enabling agents to ground time extended predicates (Fig. 1a) with complex
-spatio-temporal references to objects (Fig. 1b) and understand both present and past tenses (Fig. 1c).
-To do so we choose the third strategy mentioned above, i.e. to train a truth function that predicts when
-descriptions match traces of experience. This choice is motivated by two important considerations.
-First, prior work showed that learning truth functions was key to foster generalization [ 3], enabling
-agents to efficiently self-train policies via goal imagination [ 14] and goal relabeling [ 12]. Hence the
-truth function is an important and self-contained component of larger learning systems. Second, this
-strategy allows to carefully control the distribution of experiences and descriptions perceived by the
-agent.
-2The Embodied Language Grounding problem has a relational structure. We understand the meaning of
-words by analyzing the relations they state in the world [ 18]. The relationality of spatial and temporal
-concepts has long been identified in the field of pragmatics [ 43,44] (see Supplementary Section C
-for additional discussion). Furthermore actions themselves are relations between subjects and objects,
-and can be defined in terms of affordances of the agent [ 19]. We acknowledge this and provide
-the right relational inductive bias [ 5] to our architectures through the use of Transformers [ 46]. We
-propose a formalism unifying three variants of a multi-modal transformer inspired by Ding et al.
-[16] that implement different relational operations through the use of hierarchical attention. We
-measure the generalization capabilities of these architectures along three axes 1) generalization to
-new traces of experience; 2) generalization to randomly held out sentences; 3) generalization to
-grammar primitives, systematically held out from the training set as in Ruis et al. [37]. We observe
-that maintaining object identity in the attention computation of our Transformers is instrumental to
-achieving good performance on generalization overall. We also identify specific relational operations
-that are key to generalize on certain grammar primitives.
-Contributions. This paper introduces:
-1. A new Embodied Language Grounding task focusing on spatio-temporal language;
-2.A formalism unifying different relational architectures based on Transformers expressed as
-a function of mapping and aggregation operations;
-3.A systematic study of the generalization capabilities of these architectures and the identifica-
-tion of key components for their success on this task.
-2 Methods
-2.1 Problem Definition
-We consider the setting of an embodied agent behaving in an environment. This agent interacts with
-the surrounding objects over time, during an episode of fixed length ( T). Once this episode is over, an
-oracle provides exhaustive feedback in a synthetic language about everything that has happened. This
-language describes actions of the agent over the objects and includes spatial and temporal concepts.
-The spatial concepts are reference to an object through its spatial relation with others (Fig. 1a), and
-the temporal concepts are the past modality for the actions of the agent (Fig. 1c), past modality for
-spatial relations (Fig. 1b), and actions that unfold over time intervals. The histories of states of the
-agent’s body and of the objects over the episode as well as the associated sentences are recorded in
-a bufferB. From this setting, and echoing previous work on training agents from descriptions, we
-frame the Embodied Language Grounding problem as learning a parametrized truth function Rover
-couples of observations traces and sentences, tasked with predicting whether a given sentence Wis
-true of a given episode history Sor not. Formally, we aim to minimize:
-E(S;W )B
-L(R(S;W);r(S;W))
-whereLdenotes the cross-entropy loss and rdenotes the ground truth boolean value for sentence W
-about traceS.
-2.2 Temporal Playground
-In the absence of any dedicated dataset providing spatio-temporal descriptions from behavioral traces
-of an agent, we introduce Temporal Playground (Fig. 1) an environment coupled with a templated
-grammar designed to study spatio-temporal language grounding. The environment is a 2D world,
-with procedurally-generated scene containing N= 3objects sampled from 32 different object types
-belonging to 5 categories. Each object has a continuous 2D position, a size, a continuous color
-code specified by a 3D vector in RGB space, a type specified by a one-hot vector, and a boolean
-unit specifying whether it is grasped. The size of the object feature vector ( o) isjoj= 39 . The
-agent’s body has its 2D position in the environment and its gripper state (grasping or non-grasping) as
-features (body feature vector ( b) of sizejbj= 3). In this environment, the agent can perform various
-actions over the length ( T) of an episode. Some of the objects (the animals) can move independently.
-Objects can also interact: if the agent brings food or water to an animal, it will grow in size; similarly,
-if water is brought to a plant, it will grow. At the end of an episode, a generative grammar generates
-3sentences describing all the interactions that occurred. A complete specification of the environment
-as well as the BNF of the grammar can be found in Supplementary Section A.2.
-Synthetic language. To enable a controlled and systematic study of how different types of spatio-
-temporal linguistic meanings can be learned, we argue it is necessary to first conduct a systematic
-study with a controlled synthetic grammar. We thus consider a synthetic language with a vocabulary of
-size53and sentences with a maximum length of 8. This synthetic language facilitates the generation
-of descriptions matching behavioral traces of the agent. Moreover, it allows us to express four
-categories of concepts associated with specific words. Thus, the generated sentences consist in four
-conceptual types based on the words they involve:
-•Sentences involving basic concepts. This category of sentences talk about present-time
-events by referring to objects and their attributes. Sentences begin with the ’grasp’ token
-combined with any object. Objects can be named after their category (eg. ’animal’, ’thing’ )
-or directly by their type ( ’dog’, ’door’, ’algae’, etc. ). Finally, the color (’red’, ’blue’, ’green’ )
-of objects can also be specified.
-•Sentences involving spatial concepts. This category of sentences additionally involve
-one-to-one spatial relations and one-to-all spatial relations to refer to objects. An object
-can be ’left of’ another object (reference is made in relation to a single other object), or
-can be the ’top most’ object (reference is made in relation with all other objects). Example
-sentences include ’grasp thing bottom of cat’ or’grasp thing right most’ .
-•Sentences involving temporal concepts . This category of sentences involves talking about
-temporally-extended predicates and the past tense, without any spatial relations. The two
-temporal predicates are denoted with the words ’grow’ and’shake’ . The truth value of these
-predicates can only be decided by looking at the temporal evolution of the object’s size and
-position respectively. A predicate is transposed at the past tense if the action it describes
-was true at some point in the past and is no longer true in the present, this is indicated by
-adding the modifier ’was’ before the predicate. Example sentences include ’was grasp red
-chameleon’ (indicating that the agent grasped the red chameleon and then released it) and
-’shake bush’ ;
-•Sentences involving spatio-temporal concepts . Finally, we consider the broad class of
-spatio-temporal sentences that combine spatial reference and temporal or past-tense predi-
-cates. These are sentences that involve both the spatial and temporal concepts defined above.
-Additionally, there is a case of where the spatial and the temporal aspects are entangled:
-past spatial reference. This happens when an object is referred to by its previous spatial
-relationship with another object. Consider the case of an animal that was at first on the
-bottom of a table, then moved on top, and then is grasped. In this case we could refer to this
-animal as something that was previously on the bottom of the table. We use the same ’was’
-modifier as for the past tense predicates; and thus we would describe the action as ’Grasp
-thing was bottom of table’ .
-2.3 Architectures
-In this section we describe the architectures used as well as their inputs. Let one input sample to
-our model be I= (S;W), where (Si;t)i;trepresents the objects’ and body’s evolution, and (Wl)l
-represents the linguistic observations. Shas a spatial (or entity) dimension indexed by i2[0::N]and
-a temporal dimension indexed by t2[1::T]; for anyi,t,Si;tis a vector of observational features.
-Note that by convention, the trace (S0;t)trepresents the body’s features, and the traces (Si;t)t;i> 0
-represents the other objects’ features. Wis a 2-dimensional tensor indexed by the sequence l2[1::L];
-for anyl,Wl2RdWis a one-hot vector defining the word in the dictionary. The output to our models
-is a single scalar between 0and1representing the probability that the sentence encoded by Wis true
-in the observation trace S.
-Transformer Architectures. To systematically study the influence of architectural choices on
-language performance and generalization in our spatio-temporal grounded language context, we
-define a set of mapping and aggregation operations that allows us to succinctly describe different
-models in a unified framework. We define:
-4i=0
-i=2i=1
-i=3
-‘Grasp thing left most’qObject
-Encoder
-Language
-EncoderBody 
-Encoder
-Positional
-Encodingq
-...
-reducerreduce
-q ...
-rreduceq
-reduce
-rq
-reduce(c) Unstructured Transformer ( UT)
-(d) Spatial-First Transformer ( SFT)
-(e) Temporal-First Transformer (TFT)(a) Input Encoding
-qreduce
-(b) Optional Word Aggregation (-WA)
-t=2 t=1 t=0
-S0,2
-S2,1S
-WS = 
-W = 
-W WS
-S
-S WW
-W
-Figure 2: Visual summary of the architectures used. We show the details of UT,SFTand TFT
-respectively in subfigures (c), (d), (e), as well as a schematic illustration of the preprocessing phase
-(a) and the optional word-aggregation procedure (b).
-•An aggregation operation based on a Transformer model, called reduce .reduce is a
-parametrized function that takes 3 inputs: a tensor, a dimension tuple Dover which to
-reduce and a query tensor (that has to have the size of the reduced tensor). Rlayers of a
-Transformer are applied to the input-query concatenation and are then queried at the position
-corresponding to the query tokens. This produces an output reduced over the dimensions D.
-•A casting operation called cast .cast takes as input 2 tensors AandBand a dimension d.
-Ais flattened, expanded so as to fit the tensor Bin all dimensions except d, and concatenated
-along theddimension.
-•A helper expand operation called expand that takes as arguments a tensor and an integer n
-and repeats the tensor ntimes.
-Using those operations, we define three architectures: one with no particular bias ( Unstructured
-Transformer , inspired by Ding et al. [16], or UT); one with a spatial-first structural bias – objects and
-words are aggregated along the spatial dimension first ( Spatial-First Transformer orSFT); and one
-with a temporal-first structural bias – objects and words are aggregated along the temporal dimension
-first ( Temporal-First Transformer , or TFT).
-Before inputting the observations of bodies and objects Sand the language Winto any of the Trans-
-former architectures, they are projected to a common dimension (see Supplementary Section B.2 for
-more details). A positional encoding [ 46] is then added along the time dimension for observations and
-along the sequence dimension for language; and finally a one-hot vector indicating whether the vector
-is observational or linguistic is appended at the end. This produces the modified observation-language
-tuple (^S;^W). We let:
-UT(^S;^W) := reduce (cast(^S;^W;0);0;q)
-SFT(^S;^W;q) := reduce (reduce (cast(^W;^S;0);0;expand (q;T));0;q)
-TFT(^S;^W;q) := reduce (reduce (cast(^W;^S;1);1;expand (q;N+ 1));0;q)
-whereTis the number of time steps, Nis the number of objects and qis a learned query token. See
-Fig. 2 for an illustration of these architectures.
-Note that SFTand TFTare transpose versions of each other: SFTis performing aggregation over space
-first and then time, and the reverse is true for TFT. Additionally, we define a variant of each of these
-architectures where the words are aggregated before being related with the observations. We name
-these variants by appendding - WA(word-aggregation) to the name of the model (see Fig. 2 (b)).
-^W reduce (^W;0;q)
-5We examine these variants to study the effect of letting word-tokens directly interact with object-token
-through the self-attention layers vs simply aggregating all language tokens in a single embedding
-and letting this vector condition the processing of observations. The latter is commonly done in
-the language-conditioned RL and language grounding literature [ 11,3,25,37], using the language
-embedding in FiLM layers [ 36] for instance. Finding a significant effect here would encourage using
-architectures which allow direct interactions between the word tokens and the objects they refer to.
-LSTM Baselines. We also compare some LSTM -based baselines on this task; their architecture is
-described in more detail in Supplementary Section B.3.
-2.4 Data Generation, Training and Testing Procedures
-We use a bot to generate the episodes we train on. The data collected consists of 56837 trajectories of
-T= 30 time steps. Among the traces some descriptions are less frequent than others but we make
-sure to have at least 50 traces representing each of the 2672 descriptions we consider. We record
-the observed episodes and sentences in a buffer, and when training a model we sample (S;W;r )
-tuples with one observation coupled with either a true sentence from the buffer or another false
-sentence generated from the grammar. More details about the data generation can be found in
-Supplementary Section B.1.
-For each of the Transformer variants (6 models) and the LSTM baselines (2 models) we perform an
-hyper parameter search using 3 seeds in order to extract the best configuration. We extract the best
-condition for each model by measuring the mean F1on a testing set made of uniformly sampled
-descriptions from each of the categories define in section 2.2. We use the F1score because testing
-sets are imbalanced (the number of traces fulfilling each description is low). We then retrain best
-configurations over 10 seeds and report the mean and standard deviation (reported as solid black lines
-in Fig. 3 and Fig. 4) of the averaged F1score computed on each set of sentences. When statistical
-significance is reported in the text, it is systematically computed using a two-tail Welch’s t-test with
-null hypothesis 1=2, at level= 0:05[13]. Details about the training procedure and the hyper
-parameter search are provided in Supplementary Section B.4.
-3 Experiments and Results
-3.1 Generalization abilities of models on non-systematic split by categories of meaning
-In this experiment, we perform a study of generalization to new sentences from known observations.
-We divide our set of test sentences in four categories based on the categories of meanings listed in
-Section 2.2: Basic, Spatial, Spatio-Temporal and Temporal. We remove 15% of all possible sentences
-in each category from the train set and evaluate the F1 score on those sentences. The results are
-provided in Fig. 3.
-First, we notice that over all categories of meanings, all UTand TFTmodels, with or without word-
-aggregation, perform extremely well compared to the LSTM baselines, with all these four models
-achieving near-perfect test performance on the Basic sentences, with very little variability across the
-10 seeds. We then notice that all SFTvariants perform poorly on all test categories, in line or worse
-than the baselines. This is particularly visible on the spatio-temporal category, where the SFTmodels
-perform at 0:750:020whereas the baselines perform at 0:800:019. This suggests that across
-tasks, it is harmful to aggregate each scene plus the language information into a single vector. This
-may be due to the fact that objects lose their identity in this process, since information about all the
-objects becomes encoded in the same vector. This may make it difficult for the network to perform
-computations about the truth value of predicate on a single object.
-Secondly, we notice that the word-aggregation condition seems to have little effect on the performance
-on all three Transformer models. We only observe a significant effect for UTmodels on spatio-
-temporal concepts ( p-value = 2:38e-10). This suggests that the meaning of sentences can be
-adequately summarised by a single vector; while maintaining separated representations for each
-object is important for achieving good performance it seems unnecessary to do the same for linguistic
-input. However we notice during our hyperparameter search that our -WA models are not very robust
-to hyperparameter choice, with bigger variants more sensitive to the learning rate.
-6Thirdly, we observe that for our best-performing models, the basic categories of meanings are the
-easiest, with a mean score of 1:00:003across all UTand TFT models, then the spatial ones
-at0:960:020, then the temporal ones at 0:960:009, and finally the spatio-temporal ones at
-0:890:027. This effectively suggests, as we hypothesised, that sentences containing spatial relations
-or temporal concepts are harder to ground than those who do not.
-Known sentences with novel observations. We also examine the mean performance of our models
-for sentences in the training set but evaluated on a set of new observations : we generate a new set
-of rollouts on the environment, and only evaluate the model on sentences seen at train time (plots
-are reported in Supplementary Section D ). We see the performance is slightly better in this case,
-especially for the LSTM baselines ( 0:820:031versus 0:790:032), but the results are comparable
-in both cases, suggesting that the main difficulty for models lies in grounding spatio-temporal
-meanings and not in linguistic generalization for the type of generalization considered in this section.
-Figure 3: F1scores for all the models on randomly held-out sentences. F1is measured on
-separated sets representing each category of concepts defined in Section 2.2.
-3.2 Systematic generalization on withheld combinations of words
-In addition to the previous generalization studies, we perform an experiment in a harder linguistic
-generalization setting where we systematically remove binary combinations in our train set. This is in
-line with previous work on systematic generalization on deep learning models [ 29,37,26]. We create
-five test sets to examine the abilities of our models to generalize on binary combinations of words
-that have been systematically removed from the set of training sentences, but whose components have
-been seen before in other contexts. Our splits can be described by the set of forbidden combinations
-of words as:
-1.Forbidden object-attribute combinations. remove from the train set all sentences contain-
-ing’red cat’ ,’blue door’ and’green cactus’ . This tests the ability of models to recombine
-known objects with known attributes;
-2.Forbidden predicate-object combination. remove all sentences containing ’grow’ and all
-objects from the ’plant’ category. This tests the model’s ability to apply a known predicate
-to a known object in a new combination;
-3.Forbidden one-to-one relation. remove all sentences containing ’right of’ . Since the
-’right’ token is already seen as-is in the context of one-to-all relations ( ’right most’ ), and
-other one-to-one relations are observed during training, this tests the abilities of models to
-recombine known directions with in a known template;
-4.Forbidden past spatial relation. remove all sentences containing the contiguous tokens
-’was left of’ . This tests the abilities of models to transfer a known relation to the past
-modality, knowing other spatial relations in the past;
-5.Forbidden past predicate. remove all sentences containing the contiguous tokens ’was
-grasp’ . This tests the ability of the model to transfer a known predicate to the past modality,
-knowing that it has already been trained on other past-tense predicates.
-7To avoid retraining all models for each split, we create one single train set with all forbidden sentences
-removed and we test separately on all splits. We use the same hyperparameters for all models than in
-the previous experiments. The results are reported in Fig. 4.
-Figure 4: F1scores of all the models on systematic generalization splits. F1is measured on
-separated sets representing each of the forbidden combinations of word defined above.
-First we can notice that the good test scores obtained by the UTand TFTmodels on the previous
-sections are confirmed in on this experiment: they are the best performing models overall. We then
-notice that the first two splits, corresponding to new attribute-object and predicate-object combinations,
-are solved by the UTand TFTmodels, while the SFTmodels and the LSTM baselines struggle to
-achieve high scores. For the next 3 splits, which imply new spatial and temporal combinations, the
-scores overall drop significantly; we also observe much wider variability between seeds for each
-model, perhaps suggesting the various strategies adopted by the models to fit the train set have very
-different implications in terms of systematic generalization on spatial and temporal concepts. This
-very high variability between seeds on systematic generalization scores are reminiscent of the results
-obtained on the gSCAN benchmark [37].
-Additionally, for split 3, which implies combining known tokens to form a new spatial relation, we
-observe a significant drop in generalization for the word-aggregation ( WA) conditions, consistent
-across models (on average across seeds, 0:140:093,0:150:234and0:200:061for
-UT,SFTand TFTresp. with p-values <1e-04 for UTand SFT). This may be due to the fact that
-recombining any one-to-one relation with the known token right seen in the context of one-to-all
-relations requires a separate representation for each of the linguistic tokens. The same significant
-drop in performance for the WAcondition can be observed for UTand TFTin split 4, which implies
-transferring a known spatial relation to the past.
-However, very surprisingly, for split 5 – which implies transposing the known predicate grasp to the
-past tense – we observe a very strong effect in the opposite direction: the WAcondition seems to help
-generalizing to this unknown past predicate (from close-to-zero scores for all transformer models,
-theWAadds on average 0:710:186,0:450:178and0:520:183points for UT, ST and TT
-resp. and p-values <1e-05). This may be due to the fact that models without WAlearn a direct and
-systematic relationship between the grasp token and grasped objects, as indicated in their features;
-this relation is not modulated by the addition of the wasmodifier as a prefix to the sentence. Models
-do not exhibit the same behavior on split 4, which has similar structure (transfer the relation left of
-to the past). This may be due to the lack of variability in instantaneous predicates (only the grasp
-predicate); whereas there are several spatial relations (4 one-to-one, 4 one-to-all).
-Control experiment. We evaluate previously trained models on a test set containing hard negative
-examples. The aim of this experiment is to ensure that models truly identify the compositional
-structure of our spatio-temporal concepts and do not simply perform unit concept recognition. We
-select negative pairs (trajectory, description) so that the trajectories contain either the object or the
-action described in the positive example. Results are provided in Fig. 5. We observe a slight decrease
-of performances on all 5 categories (drop is less than 5%), demonstrating that the models do in fact
-represent the meaning of the sentence and not simply the presence or absence of a particular object or
-predicate.
-8Figure 5: Control experiment: F1scores for all the models on systematic generalization splits in
-the hard negative examples setting.
-4 Related Work
-The idea that agents should learn to represent and ground language in their experience of the world
-has a long history in developmental robotics [ 50,39,40,7] and was recently extended in the context
-of Language Conditioned Deep Reinforcement Learning [ 11,22,31,3]. These recent approaches
-often consider navigation [ 10,9] or object manipulation [ 1,22] tasks and are always using instructive
-language. Meanings typically refer to instantaneous actions and rarely consider spatial reference
-to objects [ 35]. Although our environment includes object manipulations, we here tackle novel
-categories of meanings involving the grounding of spatio-temporal concepts such as the past modality
-or complex spatio-temporal reference to objects.
-We evaluate our learning architectures on their ability to generalise to sets of descriptions that contain
-systematic differences with the training data so as to assess whether they correctly model grammar
-primitives. This procedure is similar to the gSCAN benchmark [ 37]. This kind of compositional
-generalisation is referred as ’systematicity’ by Hupkes et al. [26]. Environmental drivers that facilitate
-systematic generalization are also studied by Hill et al. [23]. Although Hupkes et al. [26] consider
-relational models in their work, they do not evaluate their performance on a Language Grounding
-task. Ruis et al. [37] consider an Embodied Language Grounding setup involving one form of time-
-extended meanings (adverbs), but do not consider the past modality and spatio-temporal reference to
-objects, and do not consider learning truth functions. Also, they do not consider learning architectures
-that process sequences of sensorimotor observations. To our knowledge, no previous work has
-conducted systematic generalization studies on an Embodied Language Grounding task involving
-spatio-temporal language with Transformers.
-The idea that relational architectures are relevant models for Language Grounding has been previously
-explored in the context of Visual Reasoning . They were indeed successfully applied for spatial
-reasoning in the visual question answering task CLEVR [38]. With the recent publication of the video
-reasoning dataset CLEVRER [47], those models were extended and demonstrated abilities to reason
-over spatio-temporal concepts, correctly answering causal, predictive and counterfactual questions
-[16]. In contrast to our study, these works around CLEVRER do not aim to analyze spatio-temporal
-language and therefore do not consider time-extended predicates or spatio-temporal reference to
-objects in their language, and do not study properties of systematic generalization over sets of new
-sentences.
-5 Discussion and Conclusion
-In this work, we have presented a first step towards learning Embodied Language Grounding of
-spatio-temporal concepts, framed as the problem of learning a truth function that can predict if a
-given sentence is true of temporally-extended observations of an agent interacting with a collection
-of objects. We have studied the impact of architectural choices on successful grounding of our
-artificial spatio-temporal language. We have modelled different possible choices for aggregation of
-9observations and language as hierarchical Transformer architectures. We have demonstrated that in
-our setting, it is beneficial to process temporally-extended observations and language tokens side-by-
-side, as evidenced by the good score of our Unstructured Transformer variant. However, there seems
-to be only minimal effect on performance in aggregating temporal observations along the temporal
-dimension first – compared to processing all traces and the language in an unstructured manner – as
-long as object identity is preserved. This can inform architectural design in cases where longer episode
-lengths make it impossible to store all individual timesteps for each object; our experiments provide
-evidence that a temporal summary can be used in these cases. Our experiments with systematic
-dimensions of generalization provide mixed evidence for the influence of summarizing individual
-words into a single vector, showing it can be detrimental to generalize to novel word combinations
-but also can help prevent overgeneralization of a relation between a single word and a single object
-without considering the surrounding linguistic context.
-Limitations and further work. There are several limitations of our setup which open important
-opportunities for further work. First, we have used a synthetic language that could be extended:
-for instance with more spatial relations and relations that are more than binary. Another axis for
-further research is using low-level observations. In our setting, we wanted to disentangle the effect
-of structural biases on learning spatio-temporal language from the problem of extracting objects
-from low level observations [ 6,21,17,30,8] in a consistent manner over time (object permanence
-[15,48]). Further steps in this direction are needed, and it could allow us to define richer attributes
-(related to material or texture) and richer temporal predicates (such as breaking, floating, etc). Finally,
-we use a synthetic language which is far from the richness of the natural language used by humans,
-but previous work has shown that natural language can be projected onto the subspace defined by
-synthetic language using the semantic embeddings learned by large language models [ 33]: this opens
-up be a fruitful avenue for further investigation.
-A further interesting avenue for future work would be to use the grounding provided by this reward
-function to allow autonomous language-conditioned agents to target their own goals [ 14]. In this sense,
-the truth function can be seen as a goal-achievement function or reward function. While generalization
-performance of our method is not perfect, the good overall f1 scores of our architectures imply that
-they can be directly transferred to a more complete RL setup to provide signal for policies conditioned
-on spatio-temporal language.
-Broader Impact. This work provides a step in the direction of building agents that better understand
-how language relates to the physical world; this can lead to personal robots that can better suit the
-needs of their owners because they can be interacted with using language. If successfully implemented,
-this technology can raise issues concerning automation of certain tasks resulting in loss of jobs for
-less-qualified workers.
-Links. The source code as well as the generated datasets can be found at https://github.com/
-flowersteam/spatio-temporal-language-transformers
-Acknowledgments and Disclosure of Funding
-Tristan Karch is partly funded by the French Ministère des Armées - Direction Générale de
-l’Armement. Laetitia Teodorescu is supported by Microsoft Research through its PhD Scholar-
-ship Programme. This work was performed using HPC resources from GENCI-IDRIS (Grant
-2020-A0091011996)
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-13Checklist
-1. For all authors...
-(a)Do the main claims made in the abstract and introduction accurately reflect the paper’s
-contributions and scope? [Yes] , we propose a summary of our results supporting our
-contributions in Section 5.
-(b) Did you describe the limitations of your work? [Yes] , in the concluding Section 5.
-(c)Did you discuss any potential negative societal impacts of your work? [Yes] , in a the
-Broader Impact paragraph in the Conclusion section 5.
-(d)Have you read the ethics review guidelines and ensured that your paper conforms to
-them? [Yes]
-2. If you are including theoretical results...
-(a) Did you state the full set of assumptions of all theoretical results? [N/A]
-(b) Did you include complete proofs of all theoretical results? [N/A]
-3. If you ran experiments...
-(a)Did you include the code, data, and instructions needed to reproduce the main experi-
-mental results (either in the supplemental material or as a URL)? [Yes] , the code is
-given as supplementary material.
-(b)Did you specify all the training details (e.g., data splits, hyperparameters, how they
-were chosen)? [Yes] , see Sections 3.1 and 3.2.
-(c)Did you report error bars (e.g., with respect to the random seed after running exper-
-iments multiple times)? [Yes] , all reported scores were averaged over 10 seeds and
-std is reported in all plots (Fig. 3, Fig. 4 of Section 3) and in the main text. We also
-performed statistical tests to measure significance.
-(d)Did you include the total amount of compute and the type of resources used (e.g.,
-type of GPUs, internal cluster, or cloud provider)? [Yes] , details are given in
-Supplementary Section D.2.
-4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...
-(a) If your work uses existing assets, did you cite the creators? [N/A]
-(b) Did you mention the license of the assets? [N/A]
-(c)Did you include any new assets either in the supplemental material or as a URL? [N/A]
-(d)Did you discuss whether and how consent was obtained from people whose data you’re
-using/curating? [N/A]
-(e)Did you discuss whether the data you are using/curating contains personally identifiable
-information or offensive content? [N/A]
-5. If you used crowdsourcing or conducted research with human subjects...
-(a)Did you include the full text of instructions given to participants and screenshots, if
-applicable? [N/A]
-(b)Did you describe any potential participant risks, with links to Institutional Review
-Board (IRB) approvals, if applicable? [N/A]
-(c)Did you include the estimated hourly wage paid to participants and the total amount
-spent on participant compensation? [N/A]
-14SUPPLEMENTARY MATERIAL
-A Supplementary: Temporal Playground Specifications
-A.1 Environment
-Temporal Playground is a procedurally generated environment consisting of 3 objects and an agent’s
-body. There are 32 types of objects, listed in Fig. 6 along with 5 object categories. Each object has a
-continuous 2D position, a size, a continuous color code specified by a 3D vector in RGB space, a type
-specified by a one-hot vector, and a boolean unit specifying whether it is grasped. Note that categories
-are not encoded in the objects’ features. The agent’s body has its 2D position in the environment and
-its gripper state (grasping or non-grasping) as features. The size of the body feature vector is 3 while
-the object feature vector has a size of 39. This environment is a spatio-temporal extension of the one
-used in this work [14].
-All positions are constrained within [1;1]2. The initial position of the agent is (0;0)while the
-initial object positions are randomized so that they are not in contact (d(obj1;obj 2)>0:3). Object
-sizes are sampled uniformly in [0:2;0:3], the size of the agent is 0:05. Objects can be grasped
-when the agent has nothing in hand, when it is close enough to the object center (d(agent;obj)<
-(size(agent) +size(obj))=2)and the gripper is closed ( 1,1when open). When a supply is on an
-animal or water is on a plant (contact define as distance between object being equal to the mean size
-of the two objects d= (size(obj1) +size(obj2))=2), the object will grow over time with a constant
-growth rate until it reaches the maximum size allowed for objects or until contact is lost.
-Category
-Object
-Typefurniture animal
- plantliving thing
-supply
-dog
-cat
-chameleon
-human
-flycactus
-carnivorous
-flower
-tree
-bushgrass
-algae
-tea
-rose
-bonsaiparrot
-mouse
-lion
-pig
-cowcupboard
-sink
-window
-sofa
-carpetdoor
-chair
-desk
-lamp
-tablewater
-food
-Can move independently, 
-can be grown with food 
-or waterCan be grown with water
-Figure 6: Representation of possible objects types and categories . Information about the possible
-interactions between objects are also given.
-15A.2 Language
-Grammar. The synthetic language we use can be decomposed into two components: the instanta-
-neous grammar and the temporal logic. Both are specified through the BNF given in Figure 7.
-Instantaneous grammar:
-<S>             ::= <pred> <thing_A>
-<pred>          ::= grow | grasp | shake
-<thing_A>       ::= <thing_B> | <attr> <thing_B> | thing <localizer> | 
-                    thing <localizer_all>
-<localizer>     ::= left of <thing_B> | right of <thing_B> |
-                    bottom of <thing_B> | top of <thing_B>
-<localizer_all> ::= left most | right most | bottom most | top most
-<thing_B>       ::= dog | cat | … | thing
-<attr>          ::= blue | green | red
-Temporal aspect:
-<S>             ::= was <pred> <thing_A>
-<thing_A>       ::= thing was <localizer> | thing was <localizer_all>
-Figure 7: BNF of the grammar used in Temporal Playground . The instantaneous grammar allows
-generating true sentences about predicates, spatial relations (one-to-one and one to all). These
-sentences are then processed by the temporal logic to produce the linguistic descriptions of our
-observations; this step is illustrated in the Temporal Aspect rules. See the main text for information
-on how these sentences are generated.
-Concept Definition. We split the set of all possible descriptions output by our grammar into four
-conceptual categories according to the rules given in Table 1.
-Concept BNF Size
-1. Basic<S> ::= <pred> <thing_A>
-152 <pred> ::= grasp
-<thing_A> ::= <thing_B> | <attr> <thing_B
-2. Spatial<S> ::= <pred> <thing_A>
-156 <pred> ::= grasp
-<thing_A> ::= < thing <localizer> | thing <localizer_all>
-3. Temporal<S> ::= <pred_A> <thing_A> | was <pred_B> <thing_A>
-648<pred_A> ::= grow |shake
-<pred_B> ::= grasp |grow |shake
-<thing_A> ::= <thing_B> | <attr> <thing_B>
-4. Spatio-
-Temporal<S> ::= <pred_A> <thing_A> | was <pred_B> <thing_A>
-1716<pred_C> <thing_C >
-<pred_A> ::= grow |shake
-<pred_B> ::= grasp |grow |shake
-<pred_C> ::= grasp
-<thing_A> ::= thing <localizer> | thing <localizer_all> |
-thing was <localizer> |
-thing was <localizer_all> |
-<thing_C> ::= thing was <localizer> |
-thing was <localizer_all>
-Table 1: Concept categories with their associated BNF. <thing_B> ,<attr> ,<localizer> and
-<localizer_all> are given in Fig. 7
-16B Supplementary Methods
-B.1 Data Generation
-Scripted bot. To generate the traces matching the descriptions of our grammar we define a set of
-scenarii that correspond to sequences of actions required to fulfill the predicates of our grammar,
-namely grasp ,grow andshake . Those scenarii are then conditioned on a boolean that modulates them
-to obtain a mix of predicates in the present and the past tenses. For instance, if a grasp scenario is
-sampled, there will be a 50% chance that the scenario will end with the object being grasped, leading
-to a present-tense description; and a 50% chance that the agent releases the object, yielding a past
-tense description.
-Description generation from behavioral traces of the agent. For each time step, the instanta-
-neous grammar generates the set of all true instantaneous sentences using a set of filtering operations
-similar to the one used in CLEVR [ 27], without the past predicates and past spatial relations. Then
-the temporal logic component uses these linguistic traces in the following way: if a given sentence
-for a predicate is true in a past time step and false in the present time step, the prefix token ’was’ is
-prepended to the sentence; similarly, if a given spatial relation is observed in a previous time step and
-unobserved in the present, the prefix token ’was’ is prepended to the spatial relation.
-B.2 Input Encoding
-We present the input processing in Fig. 8. At each time step t, the body feature vector btand the
-object features vector oi;t,i= 1;2;3are encoded using two single-layer neural networks whose
-output are of size h. Similarly, each of the words of the sentence describing the trace (represented
-as one-hot vectors) is encoded and projected in the dimension of size h. We concatenate to the
-vector obtained a modality token mthat defines if the output belongs to the scene (1;0)or to the
-description (0;1). We then feed the resulting vectors to a positional encoding that modulates the
-vectors according to the time step in the trace for btandoi;t,i= 1;2;3and according to the position
-of the word in the description for wl.
-We call the encoded body features ^btand it corresponds to ^S0;tof the input tensor of our model (see
-Fig. 2 in the Main document). Similarly, ^oi;t,i= 1;2;3are the encoded object features corresponding
-to^Si;t,i= 1;2;3. Finally ^wlare the encoded words and the components of tensor ^W.
-We callhthe hidden size of our models and recall that j^btj=j^oi;tj=j^wlj=h+ 2. This parameter
-is varied during the hyper-parameter search.
-Object
-EncoderLanguage
-EncoderBody 
-Encoder
-............bt
-bt
-oi,t
-oi,twl
-Positional
-EncodingPositional
-EncodingPositional
-Encoding
-wlm
-mm
-Figure 8: Input encoding. Body, words and objects are all projected in the same dimension.
-17B.3 Details on LSTM models
-To provide baseline models on our tasks we consider two LSTM variants. They are interesting
-baselines because they do not perform any relational computation except for relations between inputs
-at successive time steps. We consider the inputs as they were defined in Section 2.3 of the main paper.
-We consider two LSTM variants:
-1.LSTM -FLAT : This variant has two internal LSTM : one that processes the language and one
-that processes the scenes as concatenations of all the body and object features. This produces
-two vectors that are concatenated into one, which is then run through an MLP and a final
-softmax to produce the final output.
-2.LSTM -FACTORED : This variant independently processes the different body and object
-traces, which have previously been projected to the same dimension using a separate linear
-projection for the object and for the body. The language is processed by a separate LSTM .
-These body, object and language vectors are finally concatenated and fed to a final MLP and
-a softmax to produce the output.
-B.4 Details on Training Schedule
-Implementation Details. The architectures are trained via backpropagation using the Adam
-Optimizer[ 28]. The data is fed to the model in batches of 512 examples for 150 000 steps. We use a
-modular buffer to sample an important variety of different descriptions in each batch and to impose a
-ratio of positive samples of 0.1 for each description in each batch.
-Model implementations. We used the standard implementations of TransformerEncoderLayer and
-TransformerEncoder from pytorch version 1.7.1, as well as the default LSTM implementation. For
-initialization, we also use pytorch defaults.
-Hyper-parameter search. To pick the best set of parameters for each of our eight models, we
-train them on 18 conditions and select the best models. Note that each condition is run for 3 seeds
-and best models are selected according to their averaged F1score on randomly held-out descriptions
-(15% of the sentences in each category given in Table 1).
-Best models. Best models obtained thanks to the parameter search are given in Table 2.
-Model Learning rate Model hyperparams
-hidden size layer count head count param count
-UT 1e-4 256 4 8 1 :3M
-UT-WA 1e-5 512 4 8 14 :0M
-TFT 1e-4 256 4 4 3 :5M
-TFT-WA 1e-5 512 4 8 20 :3M
-SFT 1e-4 256 4 4 3 :5M
-SFT-WA 1e-4 256 2 8 2 :7M
-LSTM -FLAT 1e-4 512 4 N/A 15:6M
-LSTM -FACTORED 1e-4 512 4 N/A 17:6M
-Table 2: Hyperparameters. (for all models)
-Robustness to hyperparameters For some models, we have observed a lack of robustness to
-hyperparameters during our search. This translated to models learning to predict all observation-
-sentence tuples as false since the dataset is imbalanced (the proportion of true samples is 0.1). This
-behavior was systematically observed with a series of models whose hyperparameters are listed in
-Table 3. This happens with the biggest models with high learning rates, especially with the -WA
-variants.
-18Model Learning rate Model hyperparams
-hidden size layer count head count
-UT-WA 1e-4 512 4 4
-UT-WA 1e-4 512 4 8
-SFT 1e-4 512 4 4
-SFT-WA 1e-4 512 4 8
-SFT-WA 1e-4 512 2 4
-SFT-WA 1e-4 512 4 4
-TFT 1e-4 512 4 4
-TFT-WA 1e-4 512 4 8
-TFT-WA 1e-4 512 2 4
-TFT-WA 1e-4 512 4 4
-Table 3: Unstable models. Models and hyperparameters collapsing into uniform false prediction.
-C Supplementary Discussion: Formal descriptions of spatio-temporal
-meanings
-The study of spatial and temporal aspects of language has a long history in Artificial Intelligence and
-linguistics, where researchers have tried to define formally the semantics of such uses of language.
-For instance, work in temporal logic [ 2] has tried to create rigorous definitions of various temporal
-aspects of action reflected in the English language, such as logical operations on time intervals (an
-action fulfilling itself simultaneously with another, before, or after), non-action events (standing
-still for one hour), and event causality. These formal approaches have been complemented by work
-in pragmatics trying to define language user’s semantics as relates to spatial and temporal aspects
-of language. For instance, Tenbrink [43] examines the possible analogies to be made between
-relationships between objects in the spatial domain and relationships between events in a temporal
-domain, and concludes empirically that these aspects of language are not isomorphic and have their
-own specific rules. Within the same perspective, a formal ontology of space is developed in [ 4],
-whose complete system can be used to achieve contextualized interpretations of language users’
-spatial language. Spatial relations in everyday language use are also specified by the perspective
-used by the speaker; a formal account of this system is given in [ 44], where the transferability of
-these representations to temporal relations between events is also studied. These lines of work are of
-great relevance to our approach, especially the ones involving spatial relationships. We circumvent
-the problem of reference frames by placing ourselves in an absolute reference system where the x-y
-directions unambiguously define the concepts of left,right ,top,bottom ; nevertheless these studies
-would be very useful in a context where the speaker would also be embodied and speak from a
-different perspective. As for the temporal aspect, these lines of work focus on temporal relations
-between separate events, which is not the object of our study here; we are concerned about single
-actions (as opposed to several events) unfolding, in the past or present, over several time steps.
-19D Supplementary Results
-D.1 Generalization to new observations from known sentences
-Figure 9: Generalization to new traces of observations. F1 scores of all models on the train
-sentences with new observations. UTand TFToutperform other models on all four categories of
-meanings.
-D.2 Computing Resources
-This work was performed using HPC resources from GENCI-IDRIS (Grant 2020-A0091011996).
-We used 22k GPU-hours on nvidia-V100 GPUs for the development phase, hyperparameter search,
-and the main experiments.
-20

txt/2106.09564.txt

@@ -1,366 +0,0 @@
-Knowledge distillation from multi-modal to
-mono-modal segmentation networks
-Minhao Hu1;2?, Matthis Maillard2?(), Ya Zhang1(), Tommaso Ciceri2,
-Giammarco La Barbera2, Isabelle Bloch2, and Pietro Gori2
-1CMIC, Shanghai Jiao Tong University, Shanghai, China
-2LTCI, T el ecom Paris, Institut Polytechnique de Paris, France
-[email protected]
-[email protected]
-Abstract. The joint use of multiple imaging modalities for medical im-
-age segmentation has been widely studied in recent years. The fusion of
-information from dierent modalities has demonstrated to improve the
-segmentation accuracy, with respect to mono-modal segmentations, in
-several applications. However, acquiring multiple modalities is usually
-not possible in a clinical setting due to a limited number of physicians
-and scanners, and to limit costs and scan time. Most of the time, only
-one modality is acquired. In this paper, we propose KD-Net, a framework
-to transfer knowledge from a trained multi-modal network (teacher) to
-a mono-modal one (student). The proposed method is an adaptation
-of the generalized distillation framework where the student network is
-trained on a subset (1 modality) of the teacher's inputs (n modalities).
-We illustrate the eectiveness of the proposed framework in brain tumor
-segmentation with the BraTS 2018 dataset. Using dierent architectures,
-we show that the student network eectively learns from the teacher and
-always outperforms the baseline mono-modal network in terms of seg-
-mentation accuracy.
-1 Introduction
-Using multiple modalities to automatically segment medical images has become
-a common practice in several applications, such as brain tumor segmentation [11]
-or ischemic stroke lesion segmentation [10]. Since dierent image modalities can
-accentuate and better describe dierent tissues, their fusion can improve the seg-
-mentation accuracy. Although multi-modal models usually give the best results,
-it is often dicult to obtain multiple modalities in a clinical setting due to a
-limited number of physicians and scanners, and to limit costs and scan time. In
-many cases, especially for patients with pathologies or for emergency, only one
-modality is acquired.
-Two main strategies have been proposed in the literature to deal with prob-
-lems where multiple modalities are available at training time but some or most
-?The two rst authors contributed equally to this paper.arXiv:2106.09564v1  [cs.CV]  17 Jun 20212 M.Hu et al.
-of them are missing at inference time. The rst one is to train a generative model
-to synthesize the missing modalities and then perform multi-modal segmenta-
-tion. In [13], the authors have shown that using a synthesized modality helps
-improving the accuracy of classication of brain tumors. Ben Cohen et al. [1]
-generated PET images from CT scans to reduce the number of false positives in
-the detection of malignant lesions in livers. Generating a synthesized modality
-has also been shown to improve the quality of the segmentation of white matter
-hypointensities [12]. The main drawback of this strategy is that it is compu-
-tationally cumbersome, especially when many modalities are missing. In fact,
-one needs to train one generative network per missing modality in addition to a
-multi-modal segmentation network.
-The second strategy consists in learning a modality-invariant feature space
-that encodes the multi-modal information during training, and that allows for all
-possible combinations of modalities during inference. Within this second strat-
-egy, Havaei et al. proposed HeMIS [4], a model that, for each modality, trains a
-dierent feature extractor. The rst two moments of the feature maps are then
-computed and concatenated in the latent space from which a decoder is trained
-to predict the segmentation map. Dorent et al. [3], inspired by HeMIS, proposed
-U-HVED where they introduced skip-connections by considering intermediate
-layers, before each down-sampling step, as a feature map. This network outper-
-formed HeMIS on BraTS 2018 dataset. In [2], instead of fusing the layers by
-computing mean and variance, the authors learned a mapping function from the
-multiple feature maps to the latent space. They claimed that computing the
-moments to fuse the maps is not satisfactory since it makes each modality con-
-tribute equally to the nal result which is inconsistent with the fact that each
-modality highlights dierent zones. They obtained better results than HeMIS
-on BraTS 2015 dataset. This second strategy has good results only when one
-or two modalities are missing, however, when only one modality is available, it
-has worse results than a model trained on this specic modality. This kind of
-methods is therefore not suitable for a clinical setting where only one modality
-is usually acquired, such as pre-operative neurosurgery or radiotherapy.
-In this paper, in contrast to the previously presented methods, we propose a
-framework to transfer knowledge from a multi-modal network to a mono-modal
-one. The proposed method is based on generalized knowledge distillation [9]
-which is a combination of distillation [5] and privileged information [14]. Distil-
-lation has originally been designed for classication problems to make a small
-network (Student) learn from an ensemble of networks or from a large network
-(Teacher). It has been applied to image segmentation in [8,15] where the same in-
-put modalities have been used for the Teacher network and the Student network.
-In [15], the Student learns from the Teacher only thanks to a loss term between
-their outputs. In [8], the authors also constrained the intermediate layers of the
-Student to be similar to the ones of the Teacher. With a dierent perspective,
-the framework of privileged information was designed to boost the performance
-of a Student model by learning from both the training data and a Teacher model
-with privileged and additional information. In generalized knowledge distillation,KD-Net 3
-one uses distillation to extract useful knowledge from the privileged information
-of the Teacher [9]. In our case, Teacher and Student have the same architec-
-ture (i.e. same number of parameters) but the Teacher can learn from multiple
-input modalities (additional information) whereas the Student from only one.
-The proposed framework is based on two encoder-decoder networks, which have
-demonstrated to work well in image segmentation [7], one for the Student and
-one for the Teacher. Importantly, the proposed framework is generic since it
-works for dierent architectures of the encoder-decoder networks. Each encoder
-summarizes its input space to a latent representation that captures important
-information for the segmentation. Since the Teacher and the Student process
-dierent inputs but aim at extracting the same information, we make the as-
-sumption that their rst layers should be dierent, whereas the last layers and
-especially the latent representations (i.e. bottleneck) should be similar. By forc-
-ing the latent space of the Student to resemble the one of the Teacher, we make
-the hypothesis that the Student should learn from the additional information
-of the Teacher. To the best of our knowledge, this is the rst time that the
-generalized knowledge distillation strategy is adapted to guide the learning of
-a mono-modal student network using a multi-modal teacher network. We show
-the eectiveness of the proposed method using the BraTS 2018 dataset [11] for
-brain tumor segmentation.
-The paper is organized as follows. First, we present the proposed framework,
-called KD-Net and illustrated in Figure 1, and how the Student learns from the
-Teacher and the reference segmentation. Then, we present the implementation
-details and the results on the BraTS 2018 dataset [11].
-KD loss GT loss KL loss
-128×128×128×1
-128×128×128×4
-MaxPool3d Trilinear interpolation Softmax Conv3d InstanceNorm3d LeakyReLUReference 
-segmentation
-Teacher
-Student
-Fig. 1. Illustration of the proposed framework. Both Teacher and Student have the
-same architecture adapted from nnUNet [7]. First, the Teacher is trained using only
-the reference segmentation (GT loss). Then, the student network is trained using all
-proposed losses: KL loss, KD loss and GT loss.4 M.Hu et al.
-2 KD-Net
-The goal of the proposed framework is to train a mono-modal segmentation
-network (Student) by leveraging the knowledge from a well-trained multi-modal
-segmentation network (Teacher). Except for the number of input channels, both
-networks have the same encoder-decoder architecture with skip connections. The
-multi-modal input xi=fxi
-n;n= 1:::Ngis the concatenation of the Nmodalities
-for theithsample of the dataset. Let EtandDt(resp.EsandDs) denote the
-encoder and decoder parts of the Teacher (resp. Student). The Teacher network
-ft(xi) =DtEt(xi) receives as input multiple modalities whereas the student
-networkfs(xi
-k) =DsEs(xi
-k) only one modality xi
-k,kbeing a xed integer
-between 1 and N.
-We rst train the Teacher, using only the reference segmentation as target.
-Then, we train the Student using three dierent losses: the knowledge distillation
-term, the dissimilarity between the latent spaces, and the reference segmentation
-loss. Note that the weights of the Teacher are frozen during the training of the
-Student and the error of the Student is not back-propagated to the Teacher.
-The rst two terms allow the Student to learn from the Teacher by using the
-soft prediction of the latter as target and by forcing the encoded information
-(i.e. bottleneck) of the Student to be similar to the one of the Teacher. The last
-term makes the predicted segmentation of the Student similar to the reference
-segmentation.
-2.1 Generalized knowledge distillation
-Following the strategy of generalized knowledge distillation [9], we transfer useful
-knowledge from the additional information of the Teacher to the Student using
-the soft label targets of the Teacher. These are computed as follows:
-si=(ft(xi)=T) (1)
-whereis the softmax function and T, the temperature parameter, is a strictly
-positive value. The parameter Tcontrols the softness of the target, and the higher
-it is, the softer the target. The idea of using soft targets is to uncover relations
-between classes that would be harder to detect with hard labels. The eectiveness
-of using a temperature parameter to soften the labels was demonstrated in [5].
-The knowledge distillation loss is dened as:
-LKD=X
-i
-(1Dice (si;(fs(xi
-k)))) +BCE (s
-i;(fs(xi
-k))
-(2)
-whereDice is the Dice score, BCE the binary cross-entropy measure and s
-i
-the binary prediction of the teacher. We need to binarize sisince the soft labels
-cannot be used in the binary cross-entropy. The dice score ( Dice ) measures the
-similarity of the shape of two ensembles. Hence, it globally measures how the
-Teacher and Student's segmentation maps are close to each other. By contrast,
-the binary cross-entropy ( BCE ) is computed for each pixel independently andKD-Net 5
-therefore it is a local measure. We use the combination of these two terms to
-globally and locally measure the distance between the Student prediction and
-the Teacher soft labels.
-2.2 Latent space
-We speculate that Teacher and Student, having dierent inputs, should also
-encode dierently the information in the rst layers, the ones related to low-
-level image properties, such as color, texture and edges. By contrast, the deepest
-layers closer to the bottleneck, and related to higher level properties, should be
-more similar. Furthermore, we make the assumption that an encoder-decoder
-network encodes the information to correctly segment the input images in its
-latent space. Based on that, we propose to force the Student to learn from the
-additional information of the Teacher encoded in its bottleneck (and partially in
-the deepest layers) by making their latent representations as close as possible.
-To this end, we apply the Kullback-Leibler (KL) divergence as a loss function
-between the teacher and student's bottlenecks:
-LKL(p;q) =X
-iX
-jqi(j) logqi(j)
-pi(j)
-(3)
-wherepi(resp.qi) are the 
-Es(xi
-k) (respEt(xi)). Note that this function is not symmetric and we put the
-vectors in that order because we want the distribution of the Student's bottleneck
-to be similar to the one of the Teacher.
-2.3 Objective function
-We add a third term to the objective function to make the predicted segmen-
-tation as close as possible to the reference segmentation. It is the sum of the
-Dice loss (Dice ) and the binary cross-entropy ( BCE ) for the same reasons as in
-Section 2.1. We call it LGT:
-LGT=X
-i
-(1Dice (yi;(fs(xi
-k)))) +BCE (yi;(fs(xi
-k))
-: (4)
-whereyidenotes the reference segmentation of the ithsample in the dataset.
-The complete objective function is then:
-L=LKD+ (1)LGT+LKL (5)
-with2[0;1] and2R+. The imitation parameter balances the in
-of the reference segmentation with the one of the Teacher's soft labels. The
-greater the value, the greater the in
-parameter is instead needed to balance the magnitude of the KL loss with
-respect to the other two losses.6 M.Hu et al.
-3 Results and Discussion
-3.1 Dataset
-We evaluate the performance of the proposed framework on a publicly avail-
-able dataset from the BraTS 2018 Challenge [11]. It contains MR scans from
-285 patients with four modalities: T1, T2, T1 contrasted-enhanced (T1ce) and
-Flair. The goal of the challenge is to segment three sub-regions of brain tumors:
-whole tumor (WT), tumor core (TC) and enhancing tumor (ET). We apply a
-central crop of size 128 128128 and a random 
-augmentation. For each modality, only non-zero voxels have been normalized by
-subtracting the mean and dividing by standard deviation. Due to memory and
-time constraint, we subsample the images to the size 64 6464.
-3.2 Implementation details
-We adopt the encoder-decoder architecture described in Figure 1. Empirically,
-we found that the best parameters for the objective function are = 0:75,T= 5
-and= 10. We used Adam optimizer for 500 epochs with a learning rate equal
-to 0.0001 that is multiplied by 0.2 when the validation loss has not decreased
-for 50 epochs. We run a three fold cross validation on the 285 training cases
-of BraTS 2018. The training of the baseline, the Teacher or the Student takes
-approximately 12 hours on a NVIDIA P100 GPU.
-3.3 Results
-In our experiments, the Teacher uses all four modalities (T1, T2, T1ce and
-Flair concatenated) and the Student uses only T1ce. We choose T1ce for the
-Student since this is the standard modality used in pre-operative neurosurgery
-or radiotherapy.
-Model comparison: To demonstrate the eectiveness of the proposed frame-
-work, we rst compare it to a baseline model. Its architecture is the same as the
-encoder-decoder network in Figure 1 and it is trained using only the T1ce modal-
-ity as input. We also compare it to two other models, U-HVED and HeMIS, using
-only T1ce as input. Results were directly taken from [3]. The results are visible
-in Table 1. Our method outperforms U-HVED and HeMIS in the segmentation
-of all three tumor components. KD-Net also seems to obtain better results than
-the method proposed in [2] (again when using only T1ce as input). The authors
-show results on the BraTS 2015 dataset and therefore they are not directly
-comparable to KD-Net. Furthermore, we could not nd online their code. Nev-
-ertheless, the results of HeMIS [4] on BraTS 2015 (in [2]) and on BraTS 2018
-(in [3]) suggest that the observations of BraTS 2018 seem to be more dicult
-to segment. Since the method proposed in [2] has worst results than ours on a
-dataset that seems easier to segment, this should also be the case for the BraTS
-2018 dataset. However, this should be conrmed.KD-Net 7
-Table 1. Comparison of 3 models using the dice score on the tumor regions. Results
-of U-HVED and HeMIS are taken from the article [3], where the standard deviations
-were not provided.
-Model ET TC WT
-Baseline (nnUnet [7]) 68:11:27 80 :282:44 77 :061:47
-Teacher (4 modalities) 69:471:86 80 :771:18 88 :480:79
-U-HVED 65:5 66 :7 62 :4
-HeMIS 60:8 58 :5 58 :5
-Ours 71 :671:22 81 :451:25 76:981:54
-Ablation study: To evaluate the contribution of each loss term, we did an
-ablation study by removing each term from the objective function dened in
-Eq. 5. Table 2 shows the results using either 0 or 4 skip-connections both in the
-Student and Teacher networks. We observe that both the KL and KD loss im-
-proves the results with respect to the baseline model, especially for the enhanced
-tumor and tumor core. This also demonstrates that the proposed framework is
-generic and it works with dierent encoder-decoder architectures. More results
-can be found in the supplementary material.
-Table 2. Ablation study of the loss terms. We compare the results of the model
-trained with 3 dierent objective functions: the baseline using only the GT loss, KD-
-Net trained with only the KL term and KD-Net with the complete objective function.
-We also tested it with 0 or 4 skip-connections for both the Student and the Teacher.
-Skip
-connectionsModel Loss ET TC WT
-4 Baseline GT 68:11:27 80:282:44 77:061:47
-4 Teacher GT 69:471:86 80:771:18 88:480:79
-4 KD-Net GT+KL 70:001:51 80:851:82 77 :081:29
-4 KD-Net GT+KD 69:221:19 80:541:66 76:831:36
-4 KD-Net GT+KL+KD 71 :671:2281 :451:25 76:981:54
-0 Baseline GT 42:953:42 69:441:37 69:411:52
-0 Teacher GT 42:592:54 69:791:63 75:930:33
-0 KD-Net GT+KL 47 :590:98 70:961:73 71:411:2
-0 KD-Net GT+KD 44:81:1 70:122:42 70:191:4
-0 KD-Net GT+KL+KD 46:232:91 70:732:47 71 :931:26
-Qualitative results: In Figure 2, we show some qualitative results of the
-proposed framework and compare them with the ones obtained using the base-
-line method. We can see that the proposed framework allows the Student to8 M.Hu et al.
-discard some outliers and predict segmentation labels of higher quality. In the
-experiments, the student uses as input only T1ce, which clearly highlights the
-enhancing tumor. Remarkably, it seems that the Student learns more in this
-region (see Figure 2 and Table 1). The knowledge distilled from the Teacher
-seems to help the Student learn more where it is supposed to be \stronger".
-More qualitative results can be found in the supplementary material.
-Fig. 2. Qualitative results obtained using the the baseline and the proposed framework
-(Student). We show the slice of a subject with the corresponding 3 segmentation labels.
-Observations: It is important to remark that we also tried to expand the
-Student network by rst synthesizing another modality, such as the Flair, from
-the T1ce and then using it, together with the T1ce, for segmenting the tumor
-labels. Results were actually worse than the baseline and the computational
-time quite prohibitive. We also tried sharing the weights between the Teacher
-and the Student in the deepest layers of the networks to help transferring the
-knowledge. The intuition behind it was that since the bottlenecks should be the
-same, the information in the deepest layers should be handled identically. The
-results were almost identical, but slightly worse, to the ones obtained with the
-proposed framework presented in Figure 1. In this paper, we used the nnUNet[7]
-as network for the Student and Teacher, but theoretically any other encoder-
-decoder architecture, such as the one in [6], could be used.KD-Net 9
-4 Conclusions
-We present a novel framework to transfer knowledge from a multi-modal segmen-
-tation network to a mono-modal one. To this end, we propose to use a twofold
-approach. We employ the strategy of generalized knowledge distillation and, in
-addition, we also constrain the latent representation of the Student to be similar
-to the one of the Teacher. We validate our method in brain tumor segmen-
-tation, achieving better results than state-of-the-art methods when using only
-T1ce on Brats 2018. The proposed framework is generic and can be applied to
-any encoder-decoder segmentation network. The gain in segmentation accuracy
-and robustness to errors produced by the proposed framework makes it highly
-valuable for real-world clinical scenarios where only one modality is available at
-test time.
-5 Acknowledgment
-M.Hu is grateful for nancial support from China Scholarship Council.This work
-is supported by SHEITC (No. 2018-RGZN-02046), 111 plan (No. BP0719010),
-and STCSM (No. 18DZ2270700). M. Maillard was supported by a grant of IMT,
-Fondation Mines-T el ecom and Institut Carnot TSN, through the \Futur & Rup-
-tures" program.
-References
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-privileged information. In: ICLR (2016)10 M.Hu et al.
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txt/2106.12269.txt

@@ -1,827 +0,0 @@
-arXiv:2106.12269v1  [cs.AI]  23 Jun 2021Improved Acyclicity Reasoning for Bayesian Network Struct ure Learning with
-Constraint Programming
-Fulya Tr ¨osser1∗,Simon de Givry1and George Katsirelos2
-1Universit´ e de Toulouse, INRAE, UR MIAT, F-31320, Castanet -Tolosan, France
-2UMR MIA-Paris, INRAE, AgroParisTech, Univ. Paris-Saclay, 75005 Paris, France
-{fulya.ural, simon.de-givry }@inrae.fr, [email protected]
-Abstract
-Bayesian networks are probabilistic graphical mod-
-els with a wide range of application areas includ-
-ing gene regulatory networks inference, risk anal-
-ysis and image processing. Learning the structure
-of a Bayesian network (BNSL) from discrete data
-is known to be an NP-hard task with a superexpo-
-nential search space of directed acyclic graphs. In
-this work, we propose a new polynomial time algo-
-rithm for discovering a subset of all possible cluster
-cuts, a greedy algorithm for approximately solving
-the resulting linear program, and a generalised arc
-consistency algorithm for the acyclicity constraint.
-We embed these in the constraint programming-
-based branch-and-bound solver CPBayes and show
-that, despite being suboptimal, they improve per-
-formance by orders of magnitude. The resulting
-solver also compares favourably with GOBNILP, a
-state-of-the-art solver for the BNSL problem which
-solves an NP-hard problem to discover each cut and
-solves the linear program exactly.
-1 Introduction
-Towards the goal of explainable AI, Bayesian networks offer
-a rich framework for probabilistic reasoning. Bayesian Net -
-work Structure Learning (BNSL) from discrete observations
-corresponds to finding a compact model which best explains
-the data. It defines an NP-hard problem with a superexponen-
-tial search space of Directed Acyclic Graphs (DAG). Several
-constraint-based (exploiting local conditional independ ence
-tests) and score-based (exploiting a global objective form ula-
-tion) BNSL methods have been developed in the past.
-Complete methods for score-based BNSL include dynamic
-programming [Silander and Myllym¨ aki, 2006 ], heuristic
-search [Yuan and Malone, 2013; Fan and Yuan, 2015 ],
-maximum satisfiability [Berg et al. , 2014 ], branch-and-
-cut [Bartlett and Cussens, 2017 ]and constraint program-
-ming [van Beek and Hoffmann, 2015 ]. Here, we focus on
-the latter two.
-GOBNILP [Bartlett and Cussens, 2017 ]is a state-of-the-
-art solver for BNSL. It implements branch-and-cut in an in-
-∗Contact Authorteger linear programming (ILP) solver. At each node of the
-branch-and-bound tree, it generates cuts that improve the l in-
-ear relaxation. A major class of cuts generated by GOBNILP
-arecluster cuts , which identify sets of parent sets that cannot
-be used together in an acyclic graph. In order to find cluster
-cuts, GOBNILP solves an NP-hard subproblem created from
-the current optimal solution of the linear relaxation.
-CPBayes [van Beek and Hoffmann, 2015 ]is a constraint
-programming-based (CP) method for BNSL. It uses a CP
-model that exploits symmetry and dominance relations
-present in the problem, subproblem caching, and a pattern
-database to compute lower bounds, adapted from heuris-
-tic search [Fan and Yuan, 2015 ]. van Beek and Hoffmann
-showed that CPBayes is competitive with GOBNILP in many
-instances. In contrast to GOBNILP, the inference mech-
-anisms of CPBayes are very lightweight, which allows it
-to explore many orders of magnitude more nodes per time
-unit, even accounting for the fact that computing the patter n
-databases before search can sometimes consume considerabl e
-time. On the other hand, the lightweight pattern-based boun d-
-ing mechanism can take into consideration only limited in-
-formation about the current state of the search. Specificall y,
-it can take into account the current total ordering implied b y
-the DAG under construction, but no information that has been
-derived about the potential parent sets of each vertex, i.e. , the
-current domains of parent set variables.
-In this work, we derive a lower bound that is computation-
-ally cheaper than that computed by GOBNILP. We give in
-Section 3 a polynomial-time algorithm that discovers a clas s
-of cluster cuts that provably improve the linear relaxation . In
-Section 4, we give a greedy algorithm for solving the linear
-relaxation, inspired by similar algorithms for MaxSAT and
-Weighted Constraint Satisfaction Problems (WCSP). Finall y,
-in Section 5 we give an algorithm that enforces generalised
-arc consistency on the acyclicity constraint, based on pre-
-vious work by van Beek and Hoffmann, but with improved
-complexity and practical performance. In Section 6, we show
-that our implementation of these techniques in CPBayes lead s
-to significantly improved performance, both in the size of th e
-search tree explored and in runtime.
-2 Preliminaries
-We give here only minimal background on (inte-
-ger) linear programming and constraint program-ming, and refer the reader to existing literature
-[Papadimitriou and Steiglitz, 1998; Rossi et al. , 2006 ]
-for more.
-Constraint Programming
-A constraint satisfaction problem (CSP) is a tuple /a\}bracketle{tV,D,C/a\}bracketri}ht,
-whereVis a set of variables, Dis a function mapping vari-
-ables to domains and C is a set of constraints. An assignment
-AtoV′⊆Vis a mapping from each v∈V′toD(v). A
-complete assignment is an assignment to V. If an assignment
-mapsvtoa, we say it assigns v=a. A constraint is a pair
-/a\}bracketle{tS,P/a\}bracketri}ht, whereS⊆Vis the scope of the constraint and Pis
-a predicate over/producttext
-V∈SD(V)which accepts assignments to
-Sthatsatisfy the constraint. For an assignment AtoS′⊇S,
-letA′|Sbe the restriction of AtoS. We say that Asatisfies
-c=/a\}bracketle{tS,P/a\}bracketri}htifA|Ssatisfiesc. A problem is satisfied by Aif
-Asatisfies all constraints.
-For a constraint c=/a\}bracketle{tS,P/a\}bracketri}htand forv∈S,a∈D(v),
-v=ais generalized arc consistent (GAC) for cif there exists
-an assignment Athat assigns v=aand satisfies c. If for all
-v∈S,a∈D(v),v=ais GAC for c, thencis GAC. If
-all constraints are GAC, the problem is GAC. A constraint is
-associated with an algorithm fc, called the propagator for c,
-that removes (or prunes ) values from the domains of variables
-inSthat are not GAC.
-CSPs are typically solved by backtracking search, using
-propagators to reduce domains at each node and avoid parts
-of the search tree that are proved to not contain any solution s.
-Although CSPs are decision problems, the technology can be
-used to solve optimization problems like BNSL by, for ex-
-ample, using branch-and-bound and embedding the bounding
-part in a propagator. This is the approach used by CPBayes.
-Integer Linear Programming
-A linear program (LP) is the problem of finding
-min{cTx|x∈Rn∧Ax≥b∧x≥0}
-wherecandbare vectors, Ais a matrix, and xis a vector
-of variables. A feasible solution of this problem is one that
-satisfiesx∈Rn∧Ax≥b∧x≥0and an optimal solution is a
-feasible one that minimizes the objective function cTx. This
-can be found in polynomial time. A row Aicorresponds to
-an individual linear constraint and a column AT
-jto a variable.
-The dual of a linear program Pin the above form is another
-linear program D:
-max{bTy|y∈Rm∧ATy≤c∧y≥0}
-whereA,b,c are as before and yis the vector of dual vari-
-ables. Rows of the dual correspond to variables of the primal
-and vice versa. The objective value of any dual feasible so-
-lution is a lower bound on the optimum of P. WhenPis
-satisfiable, its dual is also satisfiable and the values of the ir
-optima meet. For a given feasible solution ˆxofP, the slack
-of constraint iisslackˆx(i) =AT
-ix−bi. Given a dual feasible
-solutionˆy,slackD
-ˆy(i)is the reduced cost of primal variable
-i,rcˆy(i). The reduced cost rcˆy(i)is interpreted as a lower
-bound on the amount that the dual objective would increase
-overbTˆyifxiis forced to be non-zero in the primal.An integer linear program (ILP) is a linear program in
-which we replace the constraint x∈Rnbyx∈Znand it
-is an NP-hard optimization problem.
-Bayesian Networks
-A Bayesian network is a directed graphical model B=
-/a\}bracketle{tG,P/a\}bracketri}htwhereG=/a\}bracketle{tV,E/a\}bracketri}htis a directed acyclic graph (DAG)
-called the structure of BandPare its parameters. A BN
-describes a normalised joint probability distribution. Ea ch
-vertex of the graph corresponds to a random variable and
-presence of an edge between two vertices denotes direct con-
-ditional dependence. Each vertex viis also associated with
-a Conditional Probability Distribution P(vi|parents(vi)).
-The CPDs are the parameters of B.
-The approach which we use here for learning a BN from
-data is the score-and-search method. Given a set of mul-
-tivariate discrete data I={I1,...,I N}, a scoring func-
-tionσ(G|I)measures the quality of the BN with un-
-derlying structure G. The BNSL problem asks to find a
-structure Gthat minimises σ(G|I)for some scoring
-function σand it is NP-hard [Chickering, 1995 ]. Several
-scoring functions have been proposed for this purpose, in-
-cluding BDeu [Buntine, 1991; Heckerman et al. , 1995 ]and
-BIC [Schwarz, 1978; Lam and Bacchus, 1994 ]. These func-
-tions are decomposable and can be expressed as the sum
-of local scores which only depend on the set of parents
-(from now on, parent set ) of each vertex: σF(G|I) =/summationtext
-v∈Vσv
-F(parents(v)|I)forF∈ {BDeu,BIC}. In
-this setting, we first compute local scores and then com-
-pute the structure of minimal score. Although there are
-potentially an exponential number of local scores that have
-to be computed, the number of parent sets actually con-
-sidered is often much smaller, for example because we re-
-strict the maximum cardinality of parent sets considered or
-we exploit dedicated pruning rules [de Campos and Ji, 2010;
-de Campos et al. , 2018 ]. We denote PS(v)the set of candi-
-date parent sets of vandPS−C(v)those parent sets that do
-not intersect C. In the following, we assume that local scores
-are precomputed and given as input, as is common in similar
-works. We also omit explicitly mentioning IorF, as they are
-constant for solving any given instance.
-LetCbe a set of vertices of a graph G.Cis a violated
-cluster if the parent set of each vertex v∈Cintersects C.
-Then, we can prove the following property:
-Property 1. A directed graph G=/a\}bracketle{tV,E/a\}bracketri}htis acyclic if and
-only if it contains no violated clusters, i.e., for all C⊆V,
-there exists v∈C, such that parents(v)∩C=∅.
-The GOBNILP solver [Bartlett and Cussens, 2017 ]formu-
-lates the problem as the following 0/1 ILP:
-min/summationdisplay
-v∈V,S⊆V\{v}σv(S)xv,S (1)
-s.t./summationdisplay
-S∈PS(v)xv,S= 1 ∀v∈V (2)
-/summationdisplay
-v∈C,S∈PS−C(v)xv,S≥1∀C⊆V (3)
-xv,S∈{0,1} ∀ v∈V,S∈PS(v)(4)Algorithm 1: Acyclicity Checker
-acycChecker (V, D)
-order←{}
-changes←true
-whilechanges do
-changes←false
-foreachv∈V\order do
-if∃S∈D(v)s.t.(S∩V)⊆order then
-1 order←order+v
-changes←true
-returnorder
-This ILP has a 0/1 variable xv,Sfor each candidate parent
-setSof each vertex vwherexv,S= 1means that Sis the par-
-ent set of v. The objective (1) directly encodes the decompo-
-sition of the scoring function. The constraint (2) asserts t hat
-exactly one parent set is selected for each random variable.
-Finally, the cluster inequalities (3) are violated when Cis a
-violated cluster. We denote the cluster inequality for clus ter
-Cascons(C)and the 0/1 variables involved as varsof(C).
-As there is an exponential number of these, GOBNILP gen-
-erates only those that improve the current linear relaxatio n
-and they are referred to as cluster cuts . This itself is an NP-
-hard problem [Cussens et al. , 2017 ], which GOBNILP also
-encodes and solves as an ILP. Interestingly, these inequali -
-ties are facets of the BNSL polytope [Cussens et al. , 2017 ],
-so stand to improve the relaxation significantly.
-The CPBayes solver [van Beek and Hoffmann, 2015 ]
-models BNSL as a constraint program. The CP model has a
-parent set variable for each random variable, whose domain
-is the set of possible parent sets, as well as order variables ,
-which give a total order of the variables that agrees with
-the partial order implied by the DAG. The objective is the
-same as (1). It includes channelling constraints between
-the set of variables and various symmetry breaking and
-dominance constraints. It computes a lower bound using
-two separate mechanisms: a component caching scheme
-and a pattern database that is computed before search and
-holds the optimal graphs for all orderings of partitions
-of the variables. Acyclicity is enforced using a global
-constraint with a bespoke propagator. The main routine
-of the propagator is acycChecker (Algorithm 1), which
-returns an order of all variables if the current set of domain s
-of the parent set variables may produce an acyclic graph, or
-a partially completed order if the constraint is unsatisfiab le.
-This algorithm is based on Property 1.
-Briefly, the algorithm takes the domains of the parent set
-variables as input and greedily constructs an ordering of th e
-variables, such that if variable vis later in the order than v′,
-thenv /∈parents(v′)1. It does so by trying to pick a parent
-setSfor an as yet unordered vertex such that Sis entirely
-contained in the set of previously ordered vertices2. If all
-assignments yield cyclic graphs, it will reach a point where
-1We treatorder as both a sequence and a set, as appropriate.
-2When propagating the acyclicity constraint it always holds that
-a∩V=a, so this statement is true. In section 3.1, we use the
-algorithm in a setting where this is not always the case.all remaining vertices are in a violated cluster in all possi ble
-graphs, and it will return a partially constructed order. If there
-exists an assignment that gives an acyclic graph, it will be
-possible by property 1 to select from a variable in V\order
-a parent set which does not intersect V\order , hence is a
-subset of order . The value Schosen for each variable in
-line 1 also gives a witness of such an acyclic graph.
-An immediate connection between the GOBNILP and CP-
-Bayes models is that the ILP variables xv,S,∀S∈PS(v)are
-the direct encoding [Walsh, 2000 ]of the parent set variables
-of the CP model. Therefore, we use them interchangeably,
-i.e., we can refer to the value SinD(v)asxv,S.
-3 Restricted Cluster Detection
-One of the issues hampering the performance of CPBayes is
-that it computes relatively poor lower bounds at deeper leve ls
-of the search tree. Intuitively, as the parent set variable d o-
-mains get reduced by removing values that are inconsistent
-with the current ordering, the lower bound computation dis-
-cards more information about the current state of the proble m.
-We address this by adapting the branch-and-cut approach of
-GOBNILP. However, instead of finding all violated cluster
-inequalities that may improve the LP lower bound, we only
-identify a subset of them.
-Consider the linear relaxation of the ILP (1)– (4), restrict ed
-to a subsetCof all valid cluster inequalities, i.e., with equa-
-tion (4) replaced by 0≤xv,S≤1∀v∈V,S∈PS(v)and
-with equation (3) restricted only to clusters in C. We denote
-thisLPC. We exploit the following property of this LP.
-Theorem 1. Letˆybe a dual feasible solution of LPCwith
-dual objective o. Then, if Cis a cluster such that C /∈C
-and the reduced cost rcof all variables varsof(C)is greater
-than 0, there exists a dual feasible solution ˆyofLPC∪Cwith
-dual objective o′≥o+minrc(C)whereminrc(C) =
-minx∈varsof(C)rcˆy(x).
-Proof. The only difference from LPCtoLPC∪Cis the ex-
-tra constraint cons(C)in the primal and corresponding dual
-variableyC. In the dual, yConly appears in the dual con-
-straints of the variables varsof(C)and in the objective, al-
-ways with coefficient 1. Under the feasible dual solution
-ˆy∪{yC= 0}, these constraints have slack at least minrc(C),
-by the definition of reduced cost. Therefore, we can set
-ˆy= ˆy∪{yC=minrc(C)}, which remains feasible and
-has objective o′=o+minrc(C), as required.
-Theorem 1 gives a class of cluster cuts, which we call RC-
-clusters, for reduced-cost clusters, guaranteed to improv e the
-lower bound. Importantly, this requires only a feasible, pe r-
-haps sub-optimal, solution.
-Example 1 (Running example) .Consider a BNSL instance
-with domains as shown in Table 1 and let C=∅. Then,ˆy= 0
-leaves the reduced cost of every variable to exactly its prim al
-objective coefficient. The corresponding ˆxassigns 1 to vari-
-ables with reduced cost 0 and 0 to everything else. These are
-both optimal solutions, with cost 0 and ˆxis integral, so it is
-also a solution of the corresponding ILP . However, it is not a
-solution of the BNSL, as it contains several cycles, includi ngVariable Domain Value Cost
-0{2} 0
-1{2,4} 0
-{} 6
-2{1,3} 0
-{} 10
-3{0} 0
-{} 5
-4{2,3} 0
-{3} 1
-{2} 2
-{} 3
-Table 1: BNSL instance used as running example.
-Algorithm 2: Lower bound computation with RC-
-clusters
-lowerBoundRC (V, D,C)
-ˆy←DualSolve (LPC(D))
-while True do
-2C←V\acycChecker (V,Drc
-C,ˆy)
-3 ifC=∅then
-return/a\}bracketle{tcost(ˆy),C/a\}bracketri}ht
-C←minimise (C)
-C←C∪{ C}
-ˆy←DualImprove (ˆy,LPC(D),C)
-C={0,2,3}. The cluster inequality cons(C)is violated in
-the primal and allows the dual bound to be increased.
-We consider the problem of discovering RC-clusters within
-the CP model of CPBayes. First, we introduce the nota-
-tionLPC(D)which is LPCwith the additional constraint
-xv,S= 0 for eachS /∈D(v). Conversely, Drc
-C,ˆyis the set
-of domains minus values whose corresponding variable in
-LPC(D)has non-zero reduced cost under ˆy, i.e.,Drc
-C,ˆy=D′
-whereD′(v) ={S|S∈D(v)∧rcˆy(xv,S) = 0}. With
-this notation, for values S /∈D(v),xv,S= 1 is infeasible in
-LPC(D), hence effectively rcˆy(xv,S) =∞.
-Theorem 2. Given a collection of clusters C, a set of domains
-Dandˆy, a feasible dual solution of LPC(D), there exists
-an RC-cluster C /∈C if and only if Drc
-C,ˆydoes not admit an
-acyclic assignment.
-Proof.(⇒)LetCbe such a cluster. Since for all xv,S∈
-varsof(C), none of these are in Drc
-C,ˆy, socons(C)is violated
-and hence there is no acyclic assignment.
-(⇐)Consider once again acycChecker , in Algorithm 1.
-When it fails to find a witness of acyclicity, it has reached
-a point where order/subsetnoteqlVand for the remaining variables
-C=V\order , all allowed parent sets intersect C. So if
-acycChecker is called with Drc
-C,ˆy, all values in varsof(C)
-have reduced cost greater than 0, so Cis an RC-cluster.
-Theorem 2 shows that detecting unsatisfiability of Drc
-C,ˆyis
-enough to find an RC-cluster. Its proof also gives a way to
-extract such a cluster from acycChecker .
-Algorithm 2 shows how theorems 1 and 2 can be used
-to compute a lower bound. It is given the current set ofdomains and a set of clusters as input. It first solves the
-dual ofLPC(D), potentially suboptimally. Then, it uses
-acycChecker iteratively to determine whether there exists
-an RC-cluster Cunder the current dual solution ˆy. If that
-cluster is empty, there are no more RC-clusters, and it termi -
-nates and returns a lower bound equal to the cost of ˆyunder
-LPC(D)and an updated pool of clusters. Otherwise, it min-
-imisesC(see section 3.1), adds it to the pool of clusters and
-solves the updated LP. It does this by calling DualImprove ,
-which solves LPC(D)exploiting the fact that only the cluster
-inequality cons(C)has been added.
-Example 2. Continuing our example, consider the behav-
-ior ofacycChecker with domains Drc
-∅,ˆyafter the initial dual
-solutionˆy= 0. Since the empty set has non-zero reduced
-cost for all variables, acycChecker fails with order={},
-henceC=V. We postpone discussion of minimization for
-now, other than to observe that Ccan be minimized to C1=
-{1,2}. We add cons(C1)to the primal LP and set the dual
-variable of C1to 6 in the new dual solution ˆy1. The reduced
-costs ofx1,{}andx2,{}are decreased by 6 and, importantly,
-rcˆy1(x1,{}) = 0 . In the next iteration of lowerBoundRC ,
-acycChecker is invoked on Drc
-{C1},ˆy1and returns the clus-
-ter{0,2,3,4}. This is minimized to C2={0,2,3}. The
-parent sets in the domains of these variables that do not in-
-tersectC2arex2,{}andx3,{}, sominrc(C2) = 4 , so we add
-cons(C2)to the primal and we set the dual variable of C2
-to 4 inˆy2. This brings the dual objective to 10. The reduced
-cost ofx2,{}is 0, so in the next iteration acycChecker runs
-onDrc
-{C1,C2},ˆy2and succeeds with the order {2,0,3,4,1}, so
-the lower bound cannot be improved further. This also hap-
-pens to be the cost of the optimal structure.
-Theorem 3. Algorithm 2 terminates but is not confluent.
-Proof. It terminates because there is a finite number of cluster
-inequalities and each iteration generates one. In the extre me,
-all cluster inequalities are in Cand the test at line 3 succeeds,
-terminating the algorithm.
-To see that it is not confluent, consider an example with 3
-clustersC1={v1,v2},C2={v2,v3}andC3={v3,v4}
-and assume that the minimum reduced cost for each cluster is
-unit and comes from x2,{4}andx3,{1}, i.e., the former value
-has minimum reduced cost for C1andC2and the latter for C2
-andC3. Then, if minimisation generates first C1, the reduced
-cost ofx3,{1}is unaffected by DualImprove , so it can then
-discoverC3, to get a lower bound of 2. On the other hand,
-if minimisation generates first C2, the reduced costs of both
-x2,{4}andx3,{1}are decreased to 0 by DualImprove , so
-neitherC1norC3are RC-clusters under the new dual solution
-and the algorithm terminates with a lower bound of 1.
-Related Work. The idea of performing propagation on the
-subset of domains that have reduced cost 0 has been used
-in the V AC algorithm for WCSPs [Cooper et al. , 2010 ]. Our
-method is more light weight, as it only performs propagation
-on the acyclicity constraint, but may give worse bounds. The
-bound update mechanism in the proof of theorem 1 is also
-simpler than V AC and more akin to the “disjoint core phase”
-in core-guided MaxSAT solvers [Morgado et al. , 2013 ].3.1 Cluster Minimisation
-It is crucial for the quality of the lower bound produced by Al -
-gorithm 2 that the RC-clusters discovered by acycChecker
-are minimised, as the following example shows. Empirically ,
-omitting minimisation rendered the lower bound ineffectiv e.
-Example 3. Suppose that we attempt to use lowerBoundRC
-without cluster minimization. Then, we use the cluster
-given byacycChecker ,C1={0,1,2,3,4}. We have
-minrc(C1) = 3 , given from the empty parent set value of
-all variables. This brings the reduced cost of x4,{}to 0.
-It then proceeds to find the cluster C2={0,1,2,3}with
-minrc(C2) = 2 and decrease the reduced cost of x3,{}to
-0, thenC3={0,1,2}withminrc(C3) = 1 , which brings
-the reduced cost of x1,{}to 0. At this point, acycChecker
-succeeds with the order {4,3,1,2,0}andlowerBoundRC re-
-turns a lower bound of 6, compared to 10 with minimization.
-The order produced by acycChecker also disagrees with the
-optimum structure.
-Therefore, when we get an RC-cluster Cat line 2 of algo-
-rithm 2, we want to extract a minimal RC-cluster (with re-
-spect to set inclusion) from C, i.e., a cluster C′⊆C, such
-that for all∅⊂C′′⊂C′,C′′is not a cluster.
-Minimisation problems like this are handled with an ap-
-propriate instantiation of QuickXPlain [Junker, 2004 ]. These
-algorithms find a minimal subset of constraints, not variabl es.
-We can pose this as a constraint set minimisation problem by
-implicitly treating a variable as the constraint “this vari able is
-assigned a value” and treating acyclicity as a hard constrai nt.
-However, the property of being an RC-cluster is not mono-
-tone. For example, consider the variables {v1,v2,v3,v4}
-andˆysuch that the domains restricted to values with 0 re-
-duced cost are{{v2}},{{v1}},{{v4}},{{v3}}, respectively.
-Then{v1,v2,v3,v4},{v1,v2}and{v3,v4}are RC-clusters.
-but{v1,v2,v3}is not because the sole value in the do-
-main ofv3does not intersect{v1,v2,v3}. We instead min-
-imise the set of variables that does not admit an acyclic so-
-lution and hence contains an RC-cluster. A minimal un-
-satisfiable set that contains a cluster is an RC-cluster, so
-this allows us to use the variants of QuickXPlain. We fo-
-cus on RobustXPlain, which is called the deletion-based al-
-gorithm in SAT literature for minimising unsatisfiable sub-
-sets[Marques-Silva and Menc´ ıa, 2020 ]. The main idea of the
-algorithm is to iteratively pick a variable and categorise i t as
-either appearing in all minimal subsets of C, in which case
-we mark it as necessary, or not, in which case we discard
-it. To detect if a variable appears in all minimal unsatisfi-
-able subsets, we only have to test if omitting this variable
-yields a set with no unsatisfiable subsets, i.e., with no vio-
-lated clusters. This is given in pseudocode in Algorithm 3.
-This exploits a subtle feature of acycChecker as described
-in Algorithm 1: if it is called with a subset of V, it does not
-try to place the missing variables in the order and allows par -
-ent sets to use these missing variables. Omitting variables
-from the set given to acycChecker acts as omitting the con-
-straint that these variables be assigned a value. The com-
-plexity ofMinimiseCluster isO(n3d), wheren=|V|and
-d= max v∈V|D(v)|, a convention we adopt throughout.Algorithm 3: Find a minimal RC-cluster subset of C
-MinimiseCluster (V, D, C)
-N=∅
-whileC/\e}atio\slash=∅do
-Pickc∈C
-C←C\{c}
-C′←V\acycChecker (N∪C,D)
-ifC′=∅then
-N←N∪{c}
-else
-C←C′\N
-returnN
-4 Solving the Cluster LP
-Solving a linear program is in polynomial time, so in princip le
-DualSolve can be implemented using any of the commercial
-or free software libraries available for this. However, sol ving
-this LP using a general LP solver is too expensive in this set-
-ting. As a data point, solving the instance steelBIC with
-our modified solver took 25,016 search nodes and 45 sec-
-onds of search, and generated 5,869RC-clusters. Approx-
-imately 20% of search time was spent solving the LP using
-the greedy algorithm that we describe in this section. CPLEX
-took around 70 seconds to solve LPCwith these cluster in-
-equalities once. While this data point is not proof that solv ing
-the LP exactly is too expensive, it is a pretty strong indicat or.
-We have also not explored nearly linear time algorithms for
-solving positive LPs [Allen-Zhu and Orecchia, 2015 ].
-Our greedy algorithm is derived from theorem 1. Observe
-first thatLPCwithC=∅, i.e., only with constraints (2) has
-optimal dual solution ˆy0that assigns the dual variable yvof/summationtext
-S∈PS(v)xv,S= 1 tominS∈PS(v)σv(S). That leaves at
-least one of xv,S,S∈PS(v)with reduced cost 0 for each
-v∈V.DualSolve starts with ˆy0and then iterates over C.
-Givenˆyi−1and a cluster C, it setsˆyi= ˆyi−1ifCis not
-an RC-cluster. Otherwise, it increases the lower bound by
-c=minrc(C)and setsˆyi= ˆyi−1∪{yC=c}. It remains to
-specify the order in which we traverse C.
-We sort clusters by increasing size |C|, breaking ties by
-decreasing minimum cost of all original parent set values
-invarsof(C). This favours finding non-overlapping cluster
-cuts with high minimum cost. In section 6, we give experi-
-mental evidence that this computes better lower bounds.
-DualImprove can be implemented by discarding previ-
-ous information and calling DualSolve (LPC(D)). Instead,
-it uses the RC-cluster Cto update the solution without revis-
-iting previous clusters.
-In terms of implementation, we store varsof(C)for each
-cluster, not cons(C). During DualSolve , we maintain the
-reduced costs of variables rather than the dual solution, ot h-
-erwise computing each reduced cost would require iterating
-over all cluster inequalities that contain a variable. Spec ifi-
-cally, we maintain ∆v,S=σv(S)−rcˆy(xv,S). In order to
-test whether a cluster Cis an RC-cluster, we need to com-
-puteminrc(C). To speed this up, we associate with each
-stored cluster a support pair (v,S)corresponding to the lastminimum cost found. If rcˆy(v,S) = 0 , the cluster is not an
-RC-cluster and is skipped. Moreover, parent set domains are
-sorted by increasing score σv(S), soS≻S′⇐⇒σv(S)>
-σv(S′). We also maintain the maximum amount of cost
-transferred to the lower bound, ∆max
-v= max S∈D(v)∆v,S
-for every v∈V. We stop iterating over D(v)as soon as
-σv(S)−∆max
-v is greater than or equal to the current mini-
-mum because∀S′≻S,σv(S′)−∆v,b≥σv(S)−∆max
-v.
-In practice, on very large instances 97.6%of unproductive
-clusters are detected by support pairs and 8.6%of the current
-domains are visited for the rest3.
-To keep a bounded-memory cluster pool, we discard fre-
-quently unproductive clusters. We throw away large cluster s
-with a productive ratio#productive
-#productive +#unproductivesmaller
-than1
-1,000. Clusters of size 10 or less are always kept because
-they are often more productive and their number is bounded.
-5 GAC for the Acyclicity Constraint
-Previously, van Beek and
-Hoffmann [van Beek and Hoffmann, 2015 ]showed that
-usingacycChecker as a subroutine, one can construct a
-GAC propagator for the acyclicity constraint by probing,
-i.e., detecting unsatisfiability after assigning each indi vidual
-value and pruning those values that lead to unsatisfiability .
-acycChecker is inO(n2d), so this gives a GAC propagator
-inO(n3d2). We show here that we can enforce GAC in time
-O(n3d), a significant improvement given that dis usually
-much larger than n.
-SupposeacycChecker finds a witness of acyclicity and
-returns the order O={v1,...,v n}. Every parent set Sof
-a variable vthat is a subset of {v′|v′≺Ov}is supported
-byO. We call such values consistent with O. Consider now
-S∈D(vi)which is inconsistent with O, therefore we have to
-probe to see if it is supported. We know that during the probe,
-nothing forcesacycChecker to deviate from{v1,...,v i−1}.
-So in a successful probe, acycChecker constructs a new or-
-derO′which is identical to Oin the first i−1positions and
-in which it moves vifurther down. Then all values consistent
-withO′are supported. This suggests that instead of probing
-each value, we can probe different orders.
-Acyclicity-GAC , shown in Algorithm 4, exploits this in-
-sight. It ensures first that acycChecker can produce a valid
-orderO. For each variable v, it constructs a new order O′
-fromOso thatvis as late as possible. It then prunes all par-
-ent set values of vthat are inconsistent with O′.
-Theorem 4. Algorithm 4 enforces GAC on the Acyclicity con-
-straint in O(n3d).
-Proof. Letv∈VandS∈D(v). LetO={O1,...,O n}
-andQ={Q1,...,Q n}be two valid orders such that O
-does not support SwhereasQdoes. It is enough to show
-that we can compute from Oa new order O′that supports
-Sby pushing vtowards the end. Let Oi=Qj=vand
-letOp={O1,...,O (i−1)},Qp={Q1,...,Q (j−1)}and
-Os={Oi+1,...,O n}.
-3See the supplementary material for more.Algorithm 4: GAC propagator for acyclicity
-Acyclicity-GAC (V , D)
-O←acycChecker (V,D)
-ifO/subsetnoteqlVthen
-return Failure
-foreachv∈Vdo
-changes←true
-i←O−1(v)
-prefix←{O1,...,O i−1}
-4 whilechanges do
-changes←false
-foreachw∈O\(prefix∪{v})do
-if∃S∈D(w)s.t.S⊆prefix then
-prefix←prefix∪{w}
-changes←true
-Prune{S|S∈D(v)∧S/notsubseteqlprefix}
-return Success
-LetO′be the order Opfollowed by Qp, followed by v,
-followed by Os, keeping only the first occurrence of each
-variable when there are duplicates. O′is a valid order: Op
-is witnessed by the assignment that witnesses O,Qpby the
-assignment that witnesses Q,vbyS(as inQ) andOsby the
-assignment that witnesses O. It also supports S, as required.
-Complexity is dominated by repeating O(n)times the loop
-at line 4, which is a version of acycChecker so has complex-
-ityO(n2d)for a total O(n3d).
-6 Experimental Results
-6.1 Benchmark Description and Settings
-The datasets come from the UCI Machine Learning Reposi-
-tory4, the Bayesian Network Repository5, and the Bayesian
-Network Learning and Inference Package6. Local scores
-were computed from the datasets using B. Malone’s code7.
-BDeu and BIC scores were used for medium size instances
-(less than 64 variables) and only BIC score for large instanc es
-(above 64 variables). The maximum number of parents was
-limited to 5 for large instances (except for accidents.test
-with maximum of 8), a high value that allows even learning
-complex structures [Scanagatta et al. , 2015 ]. For example,
-jester.test has 100 random variables, a sample size of
-4,116and770,950parent set values. For medium instances,
-no restriction was applied except for some BDeu scores (limi t
-sets to 6 or 8 to complete the computation of the local scores
-within 24 hours of CPU-time [Lee and van Beek, 2017 ]).
-We have modified the C++ source of CPBayes v1.1 by
-adding our lower bound mechanism and GAC propagator.
-We call the resulting solver ELSA and have made it publicly
-available. For the evaluation, we compare with GOBNILP
-v1.6.3 using SCIP v3.2.1 with cplex v12.7.0. All compu-
-tations were performed on a single core of Intel Xeon E5-
-2680 v3 at 2.50 GHz and 256 GB of RAM with a 1-hour
-4http://archive.ics.uci.edu/ml
-5http://www.bnlearn.com/bnrepository
-6https://ipg.idsia.ch/software.php?id=132
-7http://urlearning.orgInstance |V|/summationtext|ps(v)|GOBNILP CPBayes ELSA ELSA\GAC ELSAchrono
-carpo100 BIC 60 424 0.6 78.5 (29.7) 40.6 (0.0) 40.7 (0.0) 40.6 (0.0)
-alarm1000 BIC 37 1003 1.2 204.2 (172.9) 27.8 (0.7) 28.8 (1.5) 29.9 (2.7)
-flagBDe 29 1325 4.4 19.0 (18.1) 0.9(0.1) 0.9 (0.1) 1.3 (0.5)
-wdbc BIC 31 14614 99.8 629.8 (576.6) 48.9 (1.6) 49.1 (1.7) 50.3 (3.1)
-kdd.ts 64 43584 327.6 † 1314.5 (158.2) 1405.4 (239.5) 1663.2 (512.4)
-steel BIC 28 93027 †1270.9 (1218.9) 98.0 (49.2) 99.2 (50.1) 130.0 (81.2)
-kdd.test 64 152873 1521.7 † 1475.3 (120.6) 1515.9 (128.5) 1492.4 (109.5)
-mushroom BDe 23 438186 † 176.4 (56.0) 135.4 (33.7) 137.0 (35.0) 133.7 (31.9)
-bnetflix.ts 100 446406 † 629.0 (431.4) 1065.1 (878.4) 1111.4 (931.0) 1132.4 (936.3)
-plants.test 69 520148 † †18981.9 (17224.0) 30791.2 (29073.0) †
-jester.ts 100 531961 † † 10166.0 (9697.9) 14915.9 (14470.1) 23877.6 (23325.7)
-accidents.ts 111 568160 1274.0 † 2238.7 (904.5) 2260.3 (986.1) 2221.1 (904.8)
-plants.valid 69 684141 † † 12347.6 (8509.7) 19853.1 (15963.1) †
-jester.test 100 770950 † †17637.8 (16979.2) 21284.0 (20661.9) †
-bnetflix.test 100 1103968 †3525.2 (3283.8) 8197.7 (7975.6) 8057.3 (7841.4) 7915.0 (7686.3)
-bnetflix.valid 100 1325818 †1456.6 (1097.0) 9282.0 (8950.3) 10220.5 (9898.4) 9619.7 (9257.4)
-accidents.test 111 1425966 4975.6 † 3661.7 (641.5) 4170.1 (1213.6) 3805.2 (687.6)
-Table 2: Comparison of ELSA against GOBNILP and CPBayes. Tim e limit for instances above the line is 1h, for the rest 10h. In stances are
-sorted by increasing total domain size. For variants of CPBa yes we report in parentheses time spent in search, after prep rocessing finishes. †
-indicates a timeout.
-(resp. 10-hour) CPU time limit for medium (resp. large)
-size instances. We used default settings for GOBNILP with
-no approximation in branch-and-cut ( limits/gap= 0 ). We
-used the same settings in CPBayes and ELSA for their pre-
-processing phase (partition lower bound sizes lmin,lmax and
-local search number of restarts rmin,rmax). We used two
-different settings depending on problem size |V|:lmin=
-20,lmax= 26,rmin= 50,rmax= 500 if|V|≤64, else
-lmin= 20,lmax= 20,rmin= 15,rmax= 30 .
-6.2 Evaluation
-In Table 2 we present the runtime to solve each instance to
-optimality with GOBNILP, CPBayes, and ELSA with default
-settings, without the GAC algorithm and without sorting the
-cluster pool (leaving clusters in chronological order, rat her
-than the heuristic ordering presented in Section 4). For the in-
-stances with/bardblV/bardbl≤64(resp.>64), we had a time limit of 1
-hour (resp. 10 hours). We exclude instances that were solved
-within the time limit by GOBNILP and have a search time of
-less than 10 seconds for CPBayes and all variants of ELSA.
-We also exclude 8 instances that were not solved to optimalit y
-by any method. This leaves us 17 instances to analyse here
-out of 69 total. More details are given in the supplemental
-material, available from the authors’ web pages.
-Comparison to GOBNILP. CPBayes was al-
-ready proven to be competitive to GOBNILP
-[van Beek and Hoffmann, 2015 ]. Our results in Table 2
-confirm this while showing that neither is clearly better.
-When it comes to our solver ELSA, for all the variants, all
-instances solved within the time limit by GOBNILP are
-solved, unlike CPBayes. On top of that, ELSA solves 9 more
-instances optimally.
-Comparison to CPBayes. We have made some low-level
-performance improvements in preprocessing of CPBayes, so
-for a more fair comparison, we should compare only thesearch time, shown in parentheses. ELSA takes several or-
-ders of magnitude less search time to optimally solve most
-instances, the only exception being the bnetflix instances.
-ELSA also proved optimality for 8 more instances within the
-time limit.
-Gain from GAC. The overhead of GAC pays off as the in-
-stances get larger. While we do not see either a clear im-
-provement nor a downgrade for the smaller instances, search
-time for ELSA improves by up to 47% for larger instances
-compared to ELSA \GAC.
-Gain from Cluster Ordering. We see that the ordering
-heuristic improves the bounds computed by our greedy dual
-LP algorithm significantly. Compared to not ordering the
-clusters, we see improved runtime throughout and 3 more in-
-stances solved to optimality.
-7 Conclusion
-We have presented a new set of inference techniques for
-BNSL using constraint programming, centered around the ex-
-pression of the acyclicity constraint. These new technique s
-exploit and improve on previous work on linear relaxations o f
-the acyclicity constraint and the associated propagator. T he
-resulting solver explores a different trade-off on the axis of
-strength of inference versus speed, with GOBNILP on one
-extreme and CPBayes on the other. We showed experimen-
-tally that the trade-off we achieve is a better fit than either ex-
-treme, as our solver ELSA outperforms both GOBNILP and
-CPBayes. The major obstacle towards better scalability to
-larger instances is the fact that domain sizes grow exponen-
-tially with the number of variables. This is to some degree
-unavoidable, so our future work will focus on exploiting the
-structure of these domains to improve performance.Acknowledgements
-We thank the GenoToul (Toulouse, France) Bioinformatics
-platform for its support. This work has been partly funded
-by the “Agence nationale de la Recherche” (ANR-16-CE40-
-0028 Demograph project and ANR-19-PIA3-0004 ANTI-
-DIL chair of Thomas Schiex).
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txt/2106.14625.txt

@@ -1,752 +0,0 @@
-AMU-EURANOV A at CASE 2021 Task 1: Assessing the stability of
-multilingual BERT
-L´eo Bouscarrat1;2, Antoine Bonnefoy1, C´ecile Capponi2, Carlos Ramisch2
-1EURA NOV A, Marseille, France
-2Aix Marseille Univ, Universit ´e de Toulon, CNRS, LIS, Marseille, France
-fleo.bouscarrat, antoine.bonnefoy [email protected]
-fleo.bouscarrat, cecile.capponi, carlos.ramisch [email protected]
-Abstract
-This paper explains our participation in task 1
-of the CASE 2021 shared task. This task is
-about multilingual event extraction from news.
-We focused on sub-task 4, event information
-extraction. This sub-task has a small training
-dataset and we fine-tuned a multilingual BERT
-to solve this sub-task. We studied the instabil-
-ity problem on the dataset and tried to mitigate
-it.
-1 Introduction
-Event extraction is becoming more and more impor-
-tant as the number of online news increases. This
-task consists of extracting events from documents,
-especially news. An event is defined by a group of
-entities that give some information about the event.
-Therefore, the goal of this task is to extract, for
-each event, a group of entities that define the event,
-such as the place and time of the event.
-This task is related but still different from named
-entity recognition (NER) as the issue is to group
-the entities that are related to the same event, and
-differentiate those related to different events. This
-difference makes the task harder and also compli-
-cates the annotation.
-In the case of this shared task, the type of events
-to extract is protests (H ¨urriyeto ˘glu et al., 2021a,b).
-This shared task is in the continuation of two previ-
-ous shared tasks at CLEF 2019 (H ¨urriyeto ˘glu et al.,
-2019) and AESPEN (H ¨urriyeto ˘glu et al., 2020).
-The first one deals with English event extraction
-with three sub-tasks: document classification, sen-
-tence classification, and event information extrac-
-tion. The second focuses on event sentence co-
-reference identification, whose goal is to group
-sentences related to the same events.
-This year, task 1 is composed of the four afore-
-mentioned tasks and adds another difficulty: multi-
-linguality. This year’s data is available in English,Spanish, and Portuguese. Thus, it is important to
-note that there is much more data in English than
-in the other languages. For the document classi-
-fication sub-task, to test multilingual capabilities,
-Hindi is available on the testing set only.
-We have mainly focused on the last sub-task
-(event information extraction), but we have also
-submitted results for the first and second sub-tasks
-(document and sentence classification). We used
-multilingual BERT (Devlin et al., 2019), hence-
-forth M-BERT, which is a model known to obtain
-near state-of-the-art results on many tasks. It is also
-supposed to work well for zero-or-few-shot learn-
-ing on different languages (Pires et al., 2019). We
-will see the results on these sub-tasks, especially
-for sub-task 4 where the training set available for
-Spanish and Portuguese is small.
-Thus, one of the issues with transformer-based
-models such as M-BERT is the instability on small
-datasets (Dodge et al., 2020; Ruder, 2021). The
-instability issue is the fact that by changing some
-random seeds before the learning phase but using
-the same architecture, data and hyper-parameters
-the results can have a great variance. We will look
-at some solutions to mitigate this issue, and how
-this issue is impacting our results for sub-task 4.1
-2 Tasks and data
-Sub-tasks 1 and 2 can be seen as binary sequence
-classification, where the goal is to say if a given
-sequence is part of a specific class. In our case, a
-classifier must predict whether a document contains
-information about an event for sub-task 1 or if a
-sentence contains information about an event for
-sub-task 2.
-Document and sentence classification tasks, sub-
-tasks 1 and 2, are not our main research interest.
-1Our code is available here: https://github.com/
-euranova/AMU-EURANOVA-CASE-2021arXiv:2106.14625v2  [cs.CL]  4 Aug 2021Figure 1: Example of a snippet from sub-task 4.
-Moreover, the datasets provided for these tasks
-are less interesting (reasonable amount of training
-data).
-On the other hand, sub-task 4 not only has less
-training data available but also requires more fine-
-grained token-based prediction. The goal of sub-
-task 4 is to extract event information from snippets
-that contain sentences speaking about the same
-event. H ¨urriyeto ˘glu et al. (2019) have defined that
-an event has the following information classes (ex-
-ample in Figure 1):
-•Time, which indicates when the protest took
-place,
-•Facility name, which indicates in which facil-
-ity the protest took place,
-•Organizer, which indicates who organized the
-protest,
-• Participant, which indicates who participated
-in the protest,
-•Place, which indicates where the protest took
-place in a more general area than the facility
-(city, region, ...),
-•Target, which indicates against whom or what
-the protest took place,
-•Trigger, which is a specific word or group of
-words that indicate that a protest took place
-(examples: protested, attack, ...),
-Thus, not all the snippets contain all the classes,
-and they can contain several times the same classes.
-Each information can be composed of one or sev-
-eral adjacent words. Each snippet contains infor-
-mation related to one and only one event.
-As the data is already separated into groups
-of sentences related to the same event, our ap-
-proach consists of considering a task of named
-entity recognition with the aforementioned classes.
-Multilingual BERT has already been used for multi-
-lingual named entity recognition and showed great
-results compared to state-of-the-art models (Hakala
-and Pyysalo, 2019).The data is in BIO format (Ramshaw and Mar-
-cus, 1995), where each word has a B tag or an I tag
-of a specific class or an O tag. The B tag means
-beginning and marks the beginning of a new entity.
-The tag I means inside, which has to be preceded
-by another I tag or a B tag, and marks that the word
-is inside an entity but not the first word of the entity.
-Finally, the O-tag means outside, which means the
-word is not part of an entity.
-3 System overview
-Our model is based on pre-trained multilingual
-BERT (Devlin et al., 2019). This model has been
-pretrained on multilingual Wikipedia texts. To bal-
-ance the fact that the data is not equally distributed
-between all the languages the authors used expo-
-nential smoothed weighting to under-sample the
-most present languages and over-sample the rarest
-ones. This does not perfectly balance all the lan-
-guages but it reduces the impact of low-resourced
-languages.
-The authors of the M-BERT paper shared the
-weights of a pretrained model that we use to do fine-
-tuning. Fine-tuning a model consists of taking an
-already trained model on a specific task and using
-this model as a starting point of the training for the
-task of interest. This approach has reached state-
-of-the-arts in numerous tasks. In the case of M-
-BERT, the pre-training tasks are Masked Language
-Modeling (MLM) and Next Sentence Prediction
-(NSP).
-To be able to learn our task, we add a dense layer
-on top of the outputs of M-BERT and learn it during
-the fine-tuning. All our models are fine-tuning all
-the layers of M-BERT.
-The implementation is the one from Hugging-
-Face’s ‘transformers’ library (Wolf et al., 2020). To
-train it on our data, the model is fine-tuned on each
-sub-task.
-3.1 Sub task 1 and 2
-For sub-tasks 1 and 2, we approach these tasks
-as binary sequence classification, as the goal is to
-predict whether or not a document (sub-task 1) orsentence (sub-task 2) contains relevant information
-about a protest event. Thus the size of the output
-of the dense layer is 2. We then perform an argmax
-on these values to predict a class. We use the base
-parameters in HuggingFace’s ’transformers’ library.
-The loss is a cross-entropy, the learning rate is
-handled by an AdamW optimizer (Loshchilov and
-Hutter, 2019) and the activation function is a gelu
-(Hendrycks and Gimpel, 2016). We use a dropout
-of 10% for the fully connected layers inside M-
-BERT and the attention probabilities.
-One of the issues with M-BERT is the limited
-length of the input, as it can only take 512 tokens,
-which are tokenized words. M-BERT uses the
-wordpiece tokenizer (Wu et al., 2016). A token is
-either a word if the tokenizer knows it, if it does not
-it will separate it into several sub-tokens which are
-known. For sub-task 1, as we are working with en-
-tire documents, it can be frequent that a document
-is longer than this limit and has to be broken down
-into several sub-documents. To retain contexts in
-each sub-documents we use an overlap of 150 to-
-kens, which means between two sub-documents,
-they will have 150 tokens in common. Our method
-to output a class, in this case, is as follows:
-• tokenize a document,
-•if the tokenized document is longer than
-the 512-tokens limit, create different sub-
-documents with 150-tokens overlaps between
-each sub-document,
-• generate a prediction for each sub-document,
-•average all the predictions from sub-
-documents originated from the same docu-
-ment,
-• take the argmax of the final prediction.
-3.2 Sub-task 4
-For sub-task 4, our approach is based on word
-classification where we predict a class for each
-word of the documents.
-One issue is that as words are tokenized and can
-be transformed into several sub-tokens we have to
-choose how to choose the prediction of a multi-
-token word. Our approach is to take the prediction
-of the first token composing a word as in Hakala
-and Pyysalo (2019).
-We also have to deal with the input size as some
-documents are longer than the limit. In this case,we separate them into sub-documents with an over-
-lap of 150. Our approach is:
-• tokenize a document,
-•if the tokenized document is longer than
-the 512-tokens limit, create different sub-
-documents with 150-tokens overlaps between
-each sub-document,
-• generate a prediction for each sub-document,
-•reconstruct the entire document: take the first
-and second sub-documents, average the pre-
-diction for the same tokens (from the overlap),
-keep the prediction for the others, then use
-the same process with the obtained document
-and the next sub-document. As the size of
-each sequence is 512 and the overlap is only
-150, no tokens can be in more than 2 different
-sequences,
-•take the argmax of the final prediction for each
-word.
-3.2.1 Soft macro-F1 loss
-We used a soft macro-F1 loss (Lipton et al., 2014).
-This loss is closer than categorical cross-entropy on
-BIO labels to the metric used to evaluate systems
-in the shared task. The main issue with F1 is its
-non-differentiability, so it cannot be used as is but
-must be modified to become differentiable. The F1
-score is based on precision and recall, which in turn
-are functions of the number of true positives, false
-positives, and false negatives. These quantities are
-usually defined as follows:
-tp=X
-i2tokens(pred (i)true (i))
-fp=X
-i2tokens(pred (i)(1true (i)))
-fn=X
-i2tokens((1pred (i))true (i))
-With:
-•tokens , the list of tokens in a document,
-•true(i) , 0 if the true label of the token i is of
-the negative class, 1 if the true label is of the
-positive class
-•pred(i) , 0 if the predicted label of the token i
-is of the negative class, 1 if the predicted label
-is of the positive classAs we use macro-F1 loss, we compute the F1
-score for each class where the positive class is the
-current class and negative any other class, e.g. if
-the reference class is B-trigger, then true(i)=1 for
-B-trigger and true(i)=0 for all other classes when
-macro-averaging the F1.
-We replace the binary function pred(i) by a func-
-tion outputting the predicted probability of the to-
-ken i to be of the positive class:
-soft tp=X
-i2tokens(proba (i)true (i))
-soft fp=X
-i2tokens(proba (i)(1true (i)))
-soft fn=X
-i2tokens((1proba (i))true (i))
-With proba(i) outputting the probability of the
-token i to be of the positive class, this probability is
-the predicted probability resulting from the softmax
-activation of the fine-tuning network.
-Then we compute, in a similar fashion as a nor-
-mal F1, the precision and recall using the soft defi-
-nitions of the true positive, false positive, and false
-negative. And finally we compute the F1 score with
-the given precision and recall. As a loss function is
-a criterion to be minimized whereas F1 is a score
-that we would like to maximize, the final loss is
-1F1.
-3.2.2 Recommendation for improved stability
-A known problem of Transformers-based models
-is the training instability, especially with small
-datasets (Dodge et al., 2020; Ruder, 2021). Dodge
-et al. (2020) explain that two elements that have
-much influence on the stability are the data order
-and the initialization of the prediction layer, both
-controlled by pseudo-random numbers generated
-from a seed. To study the impact of these two el-
-ements on the models’ stability, we freeze all the
-randomness on the other parts of the models and
-change only two different random seeds:
-•the data order, i.e. the different batches and
-their order. Between two runs the model will
-see the same data during each epoch but the
-batches will be different, as the batches are
-built beforehand and do not change between
-epochs,
-•the initialization of the linear layer used to
-predict the output of the model.Another recommendation to work with
-Transformers-based models and small data made
-by Mosbach et al. (2021) is to use smaller learning
-rates but compensating with more epochs. We have
-taken this into account during the hyper-parameter
-search.
-Ruder (2021) recommend using behavioral fine-
-tuning to reduce fine-tuning instabilities. It is sup-
-posed to be especially helpful to have a better ini-
-tialization of the final prediction layer. It has also al-
-ready been used on named entity recognition tasks
-(Broscheit, 2019) and has shown that it has im-
-proved results for a task with a very small training
-dataset. Thus, to do so, we need a task with the
-same number of classes, but much larger training
-datasets. As we did not find such a task, we de-
-cided to fine-tune our model on at least the different
-languages we are working with, English, Spanish
-and Portuguese. We used named entity recognition
-datasets and kept only three classes in common in
-all the datasets: person, organization, and location.
-These three types of entities can be found in the
-shared task.
-To perform this test, the training has been done
-like that:
-•the first fine-tuning is done on the concatena-
-tion of NER datasets in different languages,
-once the training is finished we save all the
-weights of the model,
-•we load the weights of the previous model,
-except for the weights of the final prediction
-layer which are randomized with a given seed,
-•we train the model on the dataset of the shared
-task.
-4 Experimental setup
-4.1 Data
-The dataset of the shared task is based on articles
-from different newspapers in different languages.
-More information about this dataset can be found
-in (H ¨urriyeto ˘glu et al., 2021a)
-For the final submissions of sub-tasks 1, 2, and 4
-we divided the dataset given for training purposes
-into two parts with 80% for training and 20% for
-evaluation during the system training phase. We
-then predicted the data given for testing purposes
-during the shared task evaluation phase. The quan-
-tity of data for each sub-task and language can be
-found in Table 1. We can note that the majority ofSub-task English Spanish Portuguese
-Sub-task 1 9,324 1,000 1,487
-Sub-task 2 22,825 2,741 1,182
-Sub-task 4 808 33 30
-Table 1: Number of elements for each sub-task for each
-language in the data given for training purposes. Docu-
-ments for sub-task 1, sentences for sub-task 2, snippet
-(group of sentences about one event) for sub-task 4.
-Dataset Train Eval Test
-CoNLL 2003 14,041 3,250 3,453
-CoNLL 2002 8,324 1,916 1,518
-HAREM 121 8 128
-Table 2: Number of elements for each dataset used in
-the behavioral fine-tuning in each split.
-the data is in English. Spanish and Portuguese are
-only a small part of the dataset.
-For all the experiments made on sub-task 4, we
-divided the dataset given for training purposes into
-three parts with 60% for training, 20% for evaluat-
-ing and 20% for testing.
-To be able to do our approach of behavioral fine-
-tuning, we needed some Named Entity Recognition
-datasets in English, Spanish and Portuguese. For
-English we used the CoNLL 2003 dataset (Tjong
-Kim Sang and De Meulder, 2003), for Spanish the
-Spanish part of the CoNLL 2002 dataset (Tjong
-Kim Sang, 2002) and for Portuguese the HAREM
-dataset (Santos et al., 2006). Each of these datasets
-had already three different splits for training, devel-
-opment and test. Information about their size can
-be found in Table 2.
-The dataset for Portuguese is pretty small com-
-pared to the two others, but the impact of the size
-can be interesting to study.
-4.2 Hyper-parameter search
-For sub-task 4, we did a hyper-parameter search
-to optimize the results. We used Ray Tune (Liaw
-et al., 2018) and the HyperOpt algorithm Bergstra
-et al. (2013). We launched 30 different trainings,
-all the information about the search space and the
-hyper-parameters can be found in A.1. The goal is
-to optimize the macro-F1 on the evaluation set.
-Our goal was to find a set of hyper-parameters
-that performs well to use always the same in the
-following experiments. We also wanted to evaluate
-the impacts of the hyper-parameters on the training.4.3 Behavioral fine-tuning
-For the first part of the behavioral fine-tuning,
-we trained an M-BERT model on the three NER
-datasets for one epoch. We only learn for one epoch
-for timing issues, as the learning on this datasets
-takes several hours. We then fine-tune the resulting
-models with the best set of hyper-parameters found
-with the hyper-parameter search.
-4.4 Stability
-To study the stability of the model and the impact
-of behavioral fine-tuning we made 6 sets of experi-
-ments with 20 experiments in each set:
-•normal fine-tuning with random data order
-and frozen initialization of final layer,
-•normal fine-tuning with frozen data order and
-random initialization of final layer,
-•normal fine-tuning with random data order
-and random initialization of final layer,
-•behavioral fine-tuning with random data order
-and frozen initialization of final layer,
-•behavioral fine-tuning with frozen data order
-and random initialization of final layer,
-•behavioral fine-tuning with random data order
-and random initialization of final layer,
-Once again it is important to note that what we
-called behavioral fine-tuning is different from be-
-havioral fine-tuning as proposed by Ruder (2021),
-as we reset the final layer. Only the weights of all
-the layers of M-BERT are modified.
-For each set of experiments we will look at
-the average of the macro-F1, as implemented in
-Nakayama (2018), and the standard deviation of
-the macro-F1 on the training dataset, on the evalua-
-tion dataset, and on three different test datasets, one
-for each language. Thus we will be able to assess
-the importance of the instability, if our approach to
-behavioral fine-tuning helps to mitigate it and if it
-has similar results across the languages.
-We can also note that in our implementation
-the batches are not randomized. They are built
-once before the learning phase and do not change,
-neither in content nor order of passage, between
-each epoch.Figure 2: (Top) Parallel coordinates plot of the 30 experiments on sub-task 4 during the hyper-parameter search in
-function of the value of the hyper-parameters and the value of the F1 on the evaluation set. Each line represents an
-experiment, and each column a specific hyper-parameter, except the last which is the value of the metric. (Bottom)
-Same plot with the worst results removed to have a better view of the best results.
-5 Results
-5.1 Hyper-parameter search
-The results of the hyper-parameter search can be
-seen in Figure 2. On the top pictures which repre-
-sent the 30 experiments, we can see that a specific
-hyper-parameter seems to impact the worst results
-(in blue). This parameter is the learning rate, we
-can see it in the red box on the top image, all the
-blue lines are at the bottom, which means these
-experiments had a small learning rate. It seems
-that we obtain the best results with a learning rate
-around 5e-05 (0.00005), lower than 1e-06 seems to
-give bad results.
-We can then focus on the bottom picture, with
-the same type of plot but with the worst results
-removed. Another hyper-parameter that seems to
-have an impact is the number of training epochs,
-40 seems better than 20. We use a high number of
-epochs as recommended by Mosbach et al. (2021)
-to limit the instability. Beyond the learning rate and
-number of epochs, it is then hard to find impactful
-hyper-parameters.
-Finally, the set of hyper-parameters that has been
-selected is:• Adafactor: True
-• Number of training epochs: 40
-• Adam beta 2: 0.99
-• Adam beta 1: 0.74
-• Maximum gradient norm: 0.17
-• Adam epsilon: 3e-08
-• Learning rate: 5e-05
-• Weight decay: 0.36
-For the stability experiments, the number of
-training epochs have been reduced to 20 for speed
-purposes. For the first part of the behavioral fine-
-tuning, the learning rate has been set to 1e-05 as
-more data were available.
-5.2 Behavioral fine-tuning
-The results on the test dataset of each model after
-one epoch of training can be found in Table 5.
-We could not compare to state-of-the-art NER
-models on these three datasets as we do not take all
-the classes (classes such as MISC were removedData Init layer Train Eval Test EN Test ES Test PT
-NRand Fix 86.11 (1.08) 69.34 (1.01) 71.80 (.85) 54.33 (3.43) 73.14 ( 1.96)
-Fix Rand 86.88 (.53) 70.03 (.63) 71.68 ( .53)55.02 (3.28) 74.51 (2.41)
-Rand Rand 86.63 (1.08) 69.56 (.97) 71.94 (.72) 54.73 (3.44) 74.08 (3.37)
-BRand Fix 85.79 (.97) 69.32 (1.00) 71.60 (.54) 54.69 (2.99) 74.01 (2.92)
-Fix Rand 86.20 (.55) 69.57 ( .51)71.80 (.58) 53.97 (3.90) 74.50 (2.67)
-Rand Rand 86.11 (.87) 69.40 (.80) 71.85 (.73) 55.51 (2.82)74.97 (2.66)
-Table 3: Average macro-F1 score, higher is better (standard deviation, lower is better) of the 20 experiments with
-the specified setup. N means normal fine-tuning and B behavioral fine-tuning. Data means data order and Init layer
-means initialization of the final layer. Rand means random, and fix refers to frozen.
-English Spanish Portuguese Hindi
-Sub-task 1 53.46 (84.55) 46.47 (77.27) 46.47 (84.00) 29.66 (78.77)
-Sub-task 2 75.64 (85.32) 76.39 (88.61) 81.61 (88.47) /
-Sub-task 4 69.96 (78.11) 56.64 (66.20) 61.87 (73.24) /
-Table 4: Score of our final submissions for each sub-task, in parenthesis the score achieved by the best scoring
-team on each sub-task.
-Dataset Test macro-F1
-CoNLL 2003 89.8
-CoNLL 2002 86.1
-HAREM 76.1
-Table 5: Macro-F1 score of the NER task on the test
-split of each dataset used in behavioral fine-tuning after
-training the base M-BERT for 1 epoch.
-before the learning phase). The metrics used on
-these datasets are not by classes, so the comparison
-cannot be made. However, the results are already
-much better than what a random classifier would
-output, thus the weights of the models should al-
-ready be better than the weights of the base model.
-5.3 Stability
-The results of the different sets of experiments can
-be found in Table 3. First, we can see that the dif-
-ference between behavioral fine-tuning and normal
-fine-tuning is not important enough to say one is
-better than the other. We can also note that the
-standard deviation is small for English, but not
-negligible for Spanish and Portuguese.
-5.4 Final submission
-The results of the final submissions can be found
-in Table 4. We can see that our results are lower
-than the best results, especially for sub-task 1 with
-a difference of between 30 to 50 macro-F1 score
-depending on the language, whereas for sub-tasks2 and 4 the difference is close to 10 macro-F1 score
-for all the languages.
-6 Conclusion
-6.1 Sub-task 1 and 2
-As we can see in Table 4, our final results for sub-
-task 1 are much lower than the best results, but for
-sub-task 2 the difference is smaller. This is interest-
-ing as the tasks are pretty similar, thus expected the
-difference between our results and the best results
-to be of the same magnitude.
-One explanation could be our approach to han-
-dle documents longer than the input of M-BERT.
-We have chosen to take the average of the sub-
-documents, but if one part of a document contains
-an event the entire document does too. We may
-have better results looking if one sub-document at
-least is considered as having an event.
-It is then hard to compare to other models as we
-have chosen to use one model for all the languages
-and we do not know the other approaches.
-6.2 Sub-task 4
-For sub-task 4 we have interesting results for all
-the languages, even for Spanish and Portuguese,
-as we were not sure that we could learn this task
-in a supervised fashion with the amount of data
-available. In a further study, we could compare our
-results with results obtained by fine-tuning mono-
-lingual models, where we fine-tune one model for
-each language with only the data of one language.This could show the impact of having data if using
-a multilingual model instead of several monolin-
-gual models improves or not the results. We do not
-expect good results for Spanish and Portuguese as
-the training dataset is pretty limited. The results
-seem to comfort the claim of (Pires et al., 2019)
-that M-BERT works well for few-shot learning on
-other languages.
-The other question for sub-task 4 was about in-
-stability. In Table 3 we can see that the instability is
-way more pronounced for Spanish and Portuguese.
-It seems logical as we have fewer data available
-in Spanish and Portuguese than in English. The
-standard deviation for Spanish and Portuguese is
-large and can have a real impact on the final re-
-sults. Finding good seeds could help to improve
-the results for Spanish and Portuguese.
-Furthermore, our approach of behavioral fine-
-tuning did not help to reduce the instabilities. It
-was expected that one of the sources of the insta-
-bility is the initialization of the prediction, and in
-our approach, the initialization of this layer is still
-random. In our approach, we only fine-tune the
-weights of M-BERT. This does not seem to work
-and reinforces the advice of Ruder (2021) that us-
-ing behavioral fine-tuning is more useful for having
-a good initialization of the final prediction layer.
-On the two sources of randomness we studied,
-data order seems the most impactful for English,
-where we have more data. Nonetheless, for Span-
-ish and Portuguese, the two sources have a large
-impact. In a further study, we could see how the
-quantity of data helps to decrease the impact of
-these sources of instabilities.
-For the final submissions, the macro-F1 score
-for English and Portuguese is beneath the average
-macro-F1 score we found during our development
-phases. This could be due to bad seeds for random-
-ness or because the splits are different. We did not
-try to find the best-performing seeds for the final
-submissions.
-Acknowledgments
-We thank Damien Fourrure, Arnaud Jacques, Guil-
-laume Stempfel and our anonymous reviewers for
-their helpful comments.
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-A Appendix
-A.1 Hyper-parameter search
-The space search for our hyper-parameter search
-was:
-•Number of training epochs: value in [20, 25,
-30, 40],
-•Weight decay: uniform distribution between
-0.001 and 1,
-•Learning rate: value in [1e-5, 2e-5, 3e-5, 4e-5,
-5e-5, 6e-5, 2e-7, 1e-7, 3e-7, 2e-8],
-• Adafactor: value in ”True”, ”False”,
-•Adam beta 1: uniform distribution between 0
-and 1,
-•Adam beta 2: uniform distribution between 0
-and 1,
-•Epsilon: value in [1e-8, 2e-8, 3e-8, 1e-9, 2e-9,
-3e-10],
-•Maximum gradient norm: uniform distribu-
-tion between 0 and 1.For the HyperOpt algorithm we used two set
-of hyper-parameters to help finding a good sub-
-space. We maximized the macro-F1 on the evalu-
-ation dataset, and set the number of initial points
-before starting the algorithm to 5.

txt/2107.03444.txt

@@ -1,1256 +0,0 @@
-Keep it Simple: Unsupervised Simplification of Multi-Paragraph Text
-Philippe Laban
-UC BerkeleyTobias Schnabel
-MicrosoftPaul N. Bennett
-MicrosoftMarti A. Hearst
-UC Berkeley
-Abstract
-This work presents Keep it Simple (KiS), a
-new approach to unsupervised text simplifica-
-tion which learns to balance a reward across
-three properties: fluency, salience and simplic-
-ity. We train the model with a novel algorithm
-to optimize the reward ( k-SCST), in which
-the model proposes several candidate simpli-
-fications, computes each candidate’s reward,
-and encourages candidates that outperform the
-mean reward. Finally, we propose a realis-
-tic text comprehension task as an evaluation
-method for text simplification. When tested on
-the English news domain, the KiS model out-
-performs strong supervised baselines by more
-than 4 SARI points, and can help people com-
-plete a comprehension task an average of 18%
-faster while retaining accuracy, when com-
-pared to the original text.
-1 Introduction
-The main objective of text simplification is to make
-a complex text accessible to a wide audience by
-increasing its readability. In contrast with text sum-
-marization – in which key content is selected to
-remain in the summary and other content is elided
-– in text simplification, ideally all relevant content
-is preserved.
-We propose that text simplification algorithms
-need to balance three properties: (1) fluency : the
-simplified text should use well-formed English sen-
-tences, (2) salience : the simplified text should relay
-the same information as the original, and (3) sim-
-plicity : the simplified text should be syntactically
-and lexically simpler than the original.
-Figure 1 provides intuition for the necessity of
-each of the three properties. It shows the origi-
-nal text and the output of the full proposed model
-compared to three reduced versions:
-Author emails: fphillab,hearst [email protected],
-fTobias.Schnabel,Paul.N.Bennett [email protected]
-Original: NASA's Curiosity rover just celebrated a major
-milestone — 3,000 days on the surface of Mars. T o mark the
-occasion, the space agency has released a stunning new
-panorama of the red planet, captured by the rover .
-Model Full: NASA's Curiosity rover has now passed 3,000
-days of travel on the surface of Mars. T o mark the milestone,
-the space agency released a huge panorama of Mars, as
-seen by the rover .
-Model No Fluency:  NASA's Curiosity rover . celebrated. A
-major milestone — 3,000 days on. The of.. T o mark. The
-space agency has. a stunning new panorama.. red planet.
-captured by . The rover . However
-Model No Salience: NASA's Curiosity rover just celebrated a
-major milestone. The space agency has released a stunning
-new panoramic of the red planet, captured by the team. It
-was by the rover's panoramic camera.
-Model No Simplicity: NASA's Curiosity rover has celebrated
-a major milestone, 3,000 days on the ground of Mars. T o
-mark the occasion, the space agency has unveiled a stunning
-new panoramic view of the red planet, captured by the rover .Figure 1: Motivating example for the KiS method,
-based on a CBS article (Lewis, 2021). We optimize a
-three-component reward: fluency, salience and simplic-
-ity. We show model outputs when trained with all three
-components, and with a missing component.
-Without Fluency , the generator has no incen-
-tive to generate full sentences, and learns it can
-boost the simplicity score by generating short
-phrases with excessive punctuation.
-Without Salience , the generator does not gain
-by covering facts in the original text, and can im-
-prove the simplicity score by learning to remove
-facts (e.g., not mentioning planet Mars by name).
-Without Simplicity , the generator is not guided
-to favor syntactically and lexically simpler re-
-writes. In Figure 1, Model No Simplicity is in fact
-more complex than the original according to read-
-ability measures.
-As we show in the related work section (Sec-
-tion 2), there are no high-quality, large datasets
-publicly released for text simplification. In this
-work, we build on recent progress of reinforcement
-learning (RL)-based training of text generators: wearXiv:2107.03444v1  [cs.CL]  7 Jul 2021formulate a reference-free reward for text simplifi-
-cation and directly optimize it, circumventing the
-need for aligned data.
-Our main contribution is the Keep it Simple
-(KiS) procedure, a novel unsupervised method for
-text simplification. Applied to the English news do-
-main, KiS outperforms several supervised models
-on common simplification metrics such as SARI
-(Xu et al., 2016) and the Flesch-Kincaid Grade
-Level (Kincaid et al., 1975).
-A second contribution is a new algorithm for RL-
-based training of text generators, k-SCST, which
-is an extension of Self-Critical Sequence Training
-(Rennie et al., 2017). For each input, we generate
-ksampled outputs (vs. 2 in SCST), and use the
-mean population reward as a baseline. We show in
-Section 4 that in our domain, k-SCST outperforms
-models trained with SCST.
-A third contribution is a novel evaluation method
-for text simplification. Based on the assumption
-that simplified text should enable faster reading
-with better understanding, we propose a realistic
-Text Comprehension task. We show that people
-reading texts simplified by KiS are able to complete
-comprehension tasks faster than comparison texts.
-Another departure from previous work is that we
-work with paragraphs as units of text. Most work
-in text simplification is done at the sentence level,
-despite work such as Zhong et al. (2020) showing
-that common simplification phenomena occur at
-the level of the paragraph, (e.g., the deletion, inser-
-tion or re-ordering of full sentences). Specifically,
-we train our models to simplify full paragraphs,
-and evaluate our models in a human evaluation on
-short documents (i.e., 3-4 paragraphs).
-Through rigorous empirical evaluation, we
-demonstrate the strong performance of our ap-
-proach; automated results show that this unsuper-
-vised approach is able to outperform strong su-
-pervised models by 4 SARI points or more. We
-publicly released the code and model checkpoints1.
-2 Related Work
-Simplification Datasets. Early datasets were first
-based on Simple Wikipedia2: WikiSmall (Zhu
-et al., 2010), later expanded into WikiLarge (Zhang
-and Lapata, 2017). Xu et al. (2015) show there are
-quality concerns with Simple Wikipedia datasets,
-1https://github.com/tingofurro/keep_
-it_simple
-2https://simple.wikipedia.org/and propose Newsela3as a replacement. Newsela
-is a project led by educators re-writing news ar-
-ticles targeting different school grade levels. We
-view Newsela as the gold-standard for our work,
-and use the public Newsela release of 1,911 groups
-of articles to design and evaluate our work. Us-
-ing a coarse paragraph alignment algorithm, we
-extract 40,000 paired simple/complex paragraphs
-targeting a separation of 4 grade levels. We call
-this dataset the paired Newsela dataset , which we
-use for analysis and baseline training.
-Seq2Seq for Simplification . Text simplifica-
-tion is most commonly framed as a sequence-to-
-sequence (seq2seq) task, leveraging model archi-
-tectures of other seq2seq tasks, such as natural ma-
-chine translation (Zhu et al., 2010; Wubben et al.,
-2012). Martin et al. (2020) introduce ACCESS, a
-finetuned Transformer model that achieves state-
-of-the-art performance on WikiLarge. ACCESS
-can customize simplifications on parameters such
-as compression rate and paraphrase amount. We
-directly compare our approach to ACCESS.
-Data availability remains one of the main lim-
-itations to seq2seq-based text simplification. We
-side-step this issue entirely by working with unsu-
-pervised data, only requiring a small dataset with
-coarse-level alignments for calibration.
-Lexical Simplification focuses on the substi-
-tution of single words or phrases with simpler
-equivalents, with diverse approaches using lexical
-databases such as WordNet (Thomas and Anderson,
-2012), to using contextualized word vectors (Qiang
-et al., 2020). These methods tend to be limited, as
-they do not consider syntactic complexity, and have
-no direct way of modeling deletions and insertions.
-We incorporate a lexical score ( LScore ) as one of
-the rewards in our simplicity component.
-Text-edit for Simplification . Recent work
-(Dong et al., 2019; Stahlberg and Kumar, 2020)
-has modeled text simplification as a text-edit task,
-learning sequences of word-edits that transform the
-input into the output. Text editing offers explain-
-ability, at the cost of added model complexity. We
-find that without explicitly representing edits, the
-KiS model easily learns to copy (using attention
-heads) and deviate from the original text. Outputs
-can be post-processed into edits, if desired.
-Unsupervised Simplification has mostly been
-limited to lexical simplification. Recently Surya
-et al. (2019) (Unsup NTS) proposed a system that
-3https://newsela.com/can perform both lexical and syntactic simplifica-
-tion, with a joint encoder, and two decoders (simple
-and complex). We directly compare our unsuper-
-vised approach to Unsup NTS.
-RL for Simplification . Prior work (Zhang and
-Lapata, 2017; Guo et al., 2018) used Reinforce-
-ment Learning (RL)-based simplification. How-
-ever, in both cases, components of the reward or
-training procedure involved reference simplifica-
-tions, requiring an aligned dataset. By designing
-a reference-free reward, we are able to train our
-model with RL without supervision.
-Evaluation of Simplification . This usually falls
-into two categories: automatic offline evaluation,
-and human evaluation. Automatic evaluations usu-
-ally involve using n-gram overlap calculations such
-as BLEU (Papineni et al., 2002) and SARI (Xu
-et al., 2016)). SARI was shown to correlate better
-with human judgements of simplicity than BLEU,
-and it has since become a standard (Zhang and Lap-
-ata, 2017; Surya et al., 2019; Martin et al., 2020). In
-our experiments, we report both SARI and BLEU.
-Human evaluation is typically done in an intrin-
-sicway – e.g., by directly rating factors like fluency,
-simplicity and relevance of model outputs (Surya
-et al., 2019; Wubben et al., 2012). In this work,
-we propose an extrinsic, task-based protocol. In
-our comprehension study, we directly measure how
-much simplified texts can help a human reader an-
-swer questions more efficiently. The closest to our
-evaluation design is that of Angrosh et al. (2014)
-with the important difference that we require par-
-ticipants to resubmit after erroneous answers. In
-pilot studies, we found this step to be crucial for
-high-quality responses.
-3 KiS Components
-In KiS, we approach unsupervised simplification as
-a (non-differentiable) reward maximization prob-
-lem. As shown in Figure 2, there are four compo-
-nents to the reward: simplicity, fluency, salience
-and guardrails which are jointly optimized. This
-is essential to avoid trivial solutions that only con-
-sider subsets. We therefore use the product of all
-components as the total reward, because the prod-
-uct is sensitive to the sharp decrease of a single
-component. For example, the triggering of a single
-guardrail leads to the zeroing of the total reward.
-Each component is normalized to the [0;1]range.
-     Generator
-           SalienceOriginal
-Text
-       Simplicity
-ScoreOptimization
-       Fluency
-           Guardrails
- Simplified
-TextFigure 2: Keep it Simple is an unsupervised training
-procedure for text simplification. The text generator
-(GPT-2) produces candidate simplifications, scored ac-
-cording to fluency ,simplicity ,salience .Guardrails en-
-force the model does not learn high-scoring shortcuts.
-def S_Score(original,simple):
-Fstart = fkgl(original)
-tgt = target_delta(Fstart)
-Fend = fkgl(simple)
-D = Fend-Fstart
-return clip(1-((D-tgt)/tgt),0,1)
-def target_delta(Fstart):
-# Line-fitted from analysis
-ifFstart < 4.0:
-return 0.1
-ifFstart < 12:
-return 0.5*Fstart-1.9
-return 0.8*Fstart-5.6
-Figure 3: SScore algorithm. fkgl computes the
-Flesch-Kincaid grade level.
-3.1 Simplicity
-The simplicity score should establish whether the
-generator’s output uses simpler language than the
-original text. We follow prior work (Ferr ´es et al.,
-2016) and organize our score into a syntactic score
-SScore , and a lexical score LScore . Syntactic sim-
-plification focuses on reducing the complexity of a
-sentence, for example by reducing the number of
-words in a clause, or reducing distant dependencies.
-In lexical simplification, the objective is to replace
-complex phrases with simpler synonyms. To pro-
-duce a single simplicity score, we take the product
-ofSScore andLScore (both in [0;1]).
-3.1.1 Syntactic Simplicity: SScore
-We measure syntactic complexity via the Flesch-
-Kincaid grade level (FKGL) as it is easy to compute
-and maps to a grade-level which also corresponds
-to the scale used by Newsela. Other readability met-
-rics such as Dale-Chall formula (Dale and Chall,
-1948), or the Gunning-Fog index (Gunning, 1969)
-could be used, and future work could examine the
-effect of choosing one readability metric over the5.0 7.5 10.0 12.5 15.0 17.5
-FKGL of original paragraph5
-0510FKGL in Newsela rewrite
-Linear approximationFigure 4: Analysis (Kernel Density Estimate plot)
-of change in Flesch-Kincaid Grade Level in the
-paired Newsela dataset. Most simple paragraphs have
-lower FKGL than the original paragraphs (positive
-
FKGL ). When the original paragraph’s FKGL is
-higher (x-axis), the change in FKGL tends to be larger
-(y-axis). We fit a linear approximation, which we use
-to compute the Sscore .
-other. Another viable option is the Lexile score
-(Smith et al., 2016), however, because its imple-
-mentation is not publicly released, we cannot use it
-during training and we report it only for evaluation
-(done manually on the Lexile Hub4).
-Figure 3 shows the SScore algorithm. We com-
-pute the original paragraph’s FKGL ( FStart ),
-used to compute a target FKGL ( tgt). The score
-is a linear ramp measuring how close the achieved
-FKGL ( Fend ) is to the target, clipped to [0;1].
-In the initial design, the target drop was a con-
-stant: 4 grade levels, independent of FStart .
-However, analysis on the paired Newsela corpus
-revealed that the target FKGL should depend on
-the initial FKGL. This makes sense intuitively: an
-already syntactically simple paragraph should not
-require further simplification, while more complex
-paragraphs require more simplification. Figure 4
-shows the positive correlation between the original
-paragraph’s FKGL and the drop of FKGL in the
-simplified text. We fit a piece-wise linear function
-to calculate the target FKGL drop from the initial
-paragraph.
-3.1.2 Lexical Simplicity: LScore
-Lexical simplicity focuses on whether words in
-the input paragraph ( W1) are more complex than
-ones in the output paragraph ( W2). We rely on the
-observation that word frequency and difficulty are
-correlated (Breland, 1996), and use word frequency
-in a large corpus of text (Brysbaert and New, 2009)
-to determine simplicity.
-4https://hub.lexile.comBecause word frequency follows a Zipf power
-law, we use Speer et al. (2018)’s log normaliza-
-tion, adjusting the frequency on a [0;8]range, with
-words at 0 being non-existent in the corpus, and 8
-for most common words. As an example, the word
-vigorous has a frequency of 3:54, while its more
-common synonym strong obtains 5:23.
-We compute the average Zipf frequency of the
-set of inserted words ( Z(W2W1)), and the set
-of deleted words ( Z(W1W2)). The difference
-
Z(W1;W2) =Z(W2W1)Z(W1W2)(1)
-should be positive. Analysis of the paired Newsela
-corpus reveals that 91% of pairs have a positive
-
Z(W1;W2), with a median value of 0:4. We use
-this median as the target Zipf shift in the LScore ,
-and use a ramp shape similar to the SScore , clipped
-between 0 and 1 (denoted as [
]+):
-LScore(W1;W2) ="
-1j
Z(W1;W2)0:4j
-0:4#+
-(2)
-3.2 Fluency
-We use two sub-components for the fluency com-
-ponent: a pre-trained language-model, and a dis-
-criminator trained dynamically with the generator.
-3.2.1 Language-Model Fluency
-Language models assign a probability to a sequence
-of words. This probability is often used to measure
-fluency of generated text (Kann et al., 2018; Salazar
-et al., 2020). The KiS fluency score is based on
-a language model in a way similar way to Laban
-et al. (2020). The language model is used to ob-
-tain a likelihood of the original paragraph ( LM(p))
-and of the generated output LM(q). We use av-
-erage log-likelihood, for numerical stability. The
-language model fluency score is then:
-LMScore(p;q) =h
-1LM(p)LM(q)
-i+
-(3)
-is a tunable hyper-parameter. If the LM(q)is
-lower thanLM(p)byor more,LMScore(p;q) =
-0. IfLM(q)is above or equal to LM(p), then
-LMScore(p;q) = 1 , and otherwise, it is a linear
-interpolation.
-We set= 1:3as it is the value for which
-thepaired Newsela dataset achieves an average
-LMScore of 0.9.3.2.2 Discriminator Fluency
-TheLMScore is static and deterministic, which can
-be limiting, as the generator can learn during train-
-ing how to adapt and exploit flaws in the language-
-model (e.g., learning to alter capitalization).
-Inspired from the Generative Adversarial Net-
-work (GAN) framework (Goodfellow et al., 2014),
-we create a dynamic discriminator, trained in con-
-junction with the generator, dynamically adapting
-the fluency score during training.
-Specifically, we use a RoBERTa model (Liu
-et al., 2019) as the basis for the discriminator, a clas-
-sifier with two labels: 1 for authentic paragraphs,
-and 0 for generator outputs.
-As the generator produces outputs, they are as-
-signed a label of 0 and added to a training buffer ,
-while the original paragraphs are assigned a label
-of 1 and added to the training buffer as well.
-Once the training buffer reaches a size of 2,000
-samples, the discriminator is trained, using 90% of
-the training buffer. We train the discriminator for
-5 epochs (details of training are in Appendix A.1).
-At the end of each epoch, we checkpoint the dis-
-criminator model. We compare the 5 checkpoints
-in terms of F-1 performance on the remaining 10%
-of the training buffer, and keep the best checkpoint
-as the new discriminator.
-The discriminator’s probability that a paragraph
-(q) is authentic is the discriminator score:
-DScore(q) =pdisc(Y= 1jX=q) (4)
-As with GANs, there is an equilibrium between
-the generator attempting to maximize the proba-
-bility of generating real outputs (“fooling” the dis-
-criminator), and the discriminator succeeding at
-distinguishing generated and authentic texts.
-3.3 Salience
-For the salience component, we use the coverage
-model introduced in the summary loop (Laban
-et al., 2020) for the domain of text summarization,
-and adapt it to the simplification domain.
-The coverage model is a Transformer-based
-model trained to look at generated text and answer
-fill-in-the-blank questions about the original text.
-The score is based on model accuracy at filling in
-the blanks: the more is filled in, the more relevant
-the generated content is, and the higher the score.
-A key element of the coverage model is its mask-
-ing procedure, which decides which words to mask.
-In the summary loop, a limited number of extractedkeywords (up to 15 words) are masked. By contrast,
-for simplification, we mask all non-stop words,
-amounting to a masking rate of about 40%.
-This change reflects a difference in expectation
-between summarization and simplification: in sum-
-marization, only key components are expected to
-be recovered from a summary, whereas in simpli-
-fication most of the original paragraph should be
-recoverable. Coverage ranges in [0;1], and refer-
-ence simplifications in the paired Newsela corpus
-obtain an average score of 0.76, confirming that
-manual simplification can achieve high coverage.
-3.4 Guardrails
-We use guardrails as simple pattern-based scores to
-avoid common pathological generation problems
-that we observed. Unlike the main components,
-guardrails are binary, giving a score of 1 (pass) un-
-less they trigger (score of 0). We use two guardrails:
-brevity and inaccuracy.
-3.4.1 Brevity guardrail
-The brevity guardrail ensures the length of gen-
-erated paragraph ( L2) falls in a range around the
-original paragraph’s length ( L1). We compute a
-compression ratio: C=L2=L1. IfCminC
-Cmax, the guardrail passes, otherwise it triggers.
-We set [Cmin;Cmax] = [0:6;1:5], because these
-values ensure the guardrail is not triggered on 98%
-of the paired Newsela dataset; this can be adapted
-depending on the application.
-3.4.2 Inaccuracy guardrail
-Modern text generation models are known to hallu-
-cinate facts (Huang et al., 2020), which has led the
-community to create models to detect and correct
-hallucinations (Cao et al., 2020; Zhang et al., 2020;
-Wang et al., 2020).
-We propose a light-weight inaccuracy detector
-as a guardrail. We use a Named Entity Recognition
-(NER) model (Honnibal et al., 2020) to extract
-entities present in the original paragraph ( E1) and
-the model’s output ( E2). We trigger the guardrail
-if an entity present in E2is not inE1.
-Even though human writers can successfully in-
-troduce new entities without creating inaccuracies
-(e.g., replacing the city La Paz with the country Bo-
-livia), we find that text generators predominantly
-introduce inaccuracies with novel entities. This
-simple heuristic can eventually be replaced once
-inaccuracy detection technology matures.2 4 6 8 10 12
-Hours of Training105
-104
-103
-102
-Total Score8-SCST
-6-SCST
-4-SCST
-SCSTFigure 5: Training KiS models comparing SCST
-withk-SCST. We try 4, 6 and 8 as values for k. In-
-creasing k improves performance and stability.
-4 KiS Training
-Rennie et al. (2017) introduced Self-Critical Se-
-quence Training (SCST) as an effective algorithm
-for reward-based training of text generators, suc-
-cessfully applying it to image captioning. The effi-
-cacy of SCST was later confirmed on other text gen-
-eration tasks such as question generation (Zhang
-and Bansal, 2019), and summarization (Celikyil-
-maz et al., 2018; Laban et al., 2020). In SCST, a
-probabilistic model is used to generate two distinct
-candidates: CS, a candidate constructed by sam-
-pling the word distribution at each step, and ^C, by
-taking the argmax of the word distribution at each
-step. Each candidate is scored, obtaining rewards
-ofRSand^R, respectively, and the loss is:
-L= (^RRS)NX
-i=0logp(wS
-ijwS
-1:::wS
-i1;P)(5)
-wherep(wS
-ij:::)represents the probability of the
-i-th word conditioned on previously generated sam-
-pled sequence according to the model, P is the input
-paragraph, and N the number of words in the gen-
-erated sequence. Intuitively, minimizing this loss
-increases the likelihood of the sampled sequence if
-RS>^R, and decreases it otherwise, both increas-
-ing the expected total reward.
-One limitation in SCST occurs when the two
-sequences achieve comparable rewards ( RS'^R):
-the loss nears zero, and the model has little to learn,
-wasting a training sample. In our experiments with
-SCST, this can occur with 30% of samples.
-We propose an extension of SCST, which we
-callk-SCST. We generate ksampled candidates
-(k > 2), compute the rewards of each candidate
-RS1;:::;RSk, as well as the mean reward achievedby this sampled population:RS= (RS1+:::+
-RSk)=k, which we use as the baseline, instead of
-^R. The lossLbecomes:
-L=kX
-j=1(RSRSj)NX
-i=0logp(wSj
-ijwSj
-1:::wSj
-i1;P)
-(6)
-We use a GPT2-medium for the generator, ini-
-tialized with the released pre-trained checkpoint.
-Experimental details such as data and optimizer
-used are provided in Appendix A.1.
-In Figure 5, we show results of a direct compar-
-ison of SCST ( k= 2) withk-SCST varying kin
-f4;6;8g, while keeping other components of the
-training fixed. Because of the variance involved in
-RL training, we recorded six independent training
-runs for each setting (for a total of 24 runs), and
-plot the average reward across runs of a setting, as
-well as the standard error of the mean (SEM).
-We observe that increasing kleads to higher
-average reward, and less variation in the reward.
-In our setting, k-SCST boosts performance and
-stabilizes training. We use k= 8in all final models,
-as increasing kfurther is impractical due to GPU
-memory limitations.
-We believek-SCST’s advantage stems from two
-factors: first, obtaining a better estimate of the
-distribution of rewards by sampling more outputs,
-second, by using the mean reward as the baseline,
-saving on computation of a separate baseline gener-
-ation. We believe k-SCST can also improve learn-
-ing in other text generation applications and plan
-to pursue this in future work.
-5 Experiments
-We present results experimentally validating the
-KiS procedure for text simplification. We give re-
-sults based on automatic metrics, on a novel human
-comprehension task, and from an ablation study.
-5.1 Models Compared
-We compare the KiS Model to three strong super-
-vised models, and an unsupervised approach.
-ACCESS from (Martin et al., 2020), is a state-
-of-the-art Transformer model trained on WikiLarge
-(300,000 pairs of complex/simple sentences). This
-model uses default parameters ( NBChar =0.95,
-LevSim =0.75).
-ACCESS90 is identical to ACCESS , with dif-
-ferent parameters ( NBChar =0.90, LevSim =0.75),
-reducing target compression from 95% to 90%,
-matching the average compression rate in Newsela.Model SARI BLEU %FKGL %Lexile Comp. Cov.
-Newsela - - 87 79 .918 .754
-Finetune Baseline .470 .719 68 52 .903 .894
-ACCESS Default .666 .649 86 63 .958 .805
-ACCESS 90 .674 .644 93 64 .921 .789
-Unsup NTS .677 .535 48 57 .753 .618
-KiS Model .709 .526 100 72 .852 .640
-Table 1: Automatic results on Newsela test-set. SARI
-andBLEU are reference-based metrics. %FKGL and
-%Lexile are percentages of model outputs lowering the
-grade level. Comp. is the average compression ratio (#
-words), and Cov. the output’s average coverage score.
-Finetune Baseline is a GPT2-medium model
-finetuned on the paired Newsela dataset . Large
-pre-trained models often perform competitively in
-low-resource environments, making this a strong
-point of comparison.
-Unsup NTS from (Surya et al., 2019) is an unsu-
-pervised approach based on successively encoding
-and denoising text using a GRU architecture.
-Training details for the KiS Model and Finetune
-Baseline are in Appendix A.1.
-5.2 Automatic Results
-We put aside 500 samples from the paired Newsela
-dataset as a test set to compare models on auto-
-matic metrics. We compare models on SARI and
-BLEU, report the percentage when readability mea-
-sures see an improvement in readability: %FKGL,
-and %Lexile and compute the average compres-
-sion rate (Comp.), and coverage (Cov.). Results are
-summarized in Table 1.
-The KiS model achieves the highest SARI score
-by a margin of 0.04, even though it is an unsuper-
-vised approach.
-Finetune Baseline achieves the highest BLEU
-and salience scores, but lowest SARI score. We
-interpret this as showing the model takes the least
-risk: high salience, with little simplification.
-We observe that all models are able to increase
-readability in terms of FKGL and Lexile compared
-to original paragraphs. We note that for almost all
-models, the percentage is lower for the Lexile mea-
-sure than for FKGL, showing that an improvement
-in Lexile score is more difficult to achieve than
-FKGL. The KiS model achieves an increase in Lex-
-ile readability 72% of the time, the closest figure
-to 79% of the Newsela human-written reference.
-We note that the perfect performance of KiS on
-%FKGL could be explained by the fact that FKGL
-is a part of a component being optimized ( SScore ),
-however Lexile was not.In terms of compression, the KiS model com-
-presses the second most, most likely hurting its
-coverage. Adjusting the Brevity guardrail could
-encourage the model to compress less. ACCESS90
-has the compression rate closest to Newsela refer-
-ences, but this only leads to a modest improvement
-in SARI when compared to ACCESS.
-Overall, the Newsela references achieve the
-best percentage of Lexile readability improvement,
-while outperforming the KiS model at coverage:
-there is still a gap between human-written simplifi-
-cations and model-generated ones.
-5.3 Human Comprehension Study
-We propose a human comprehension study to evalu-
-ate the usefulness of simplification results. Simpli-
-fied text should be easier to read than the original
-text, while retaining accuracy and understanding.
-We design a task to evaluate how well both manual
-and automated simplifications achieve this objec-
-tive. The main idea is to show readers a text and
-ask them to answer multiple-choice questions, eval-
-uating the texts based on time and retries needed to
-select the correct answer.
-5.3.1 Study Design
-Five different versions of each document were
-generated as stimuli: the original document, the
-Newsela reference, and versions from the three
-best-performing methods from the last section:
-KiS, Finetune Baseline, and ACCESS. We did not
-include Unsup NTS in our analysis, because of its
-low performance on %FKGL and %Lexile metrics.
-Associated with each document are five manually
-generated multiple-choice questions, each with one
-or more correct answers and one to four distractors.
-The original and the Newsela texts were checked
-manually by experimenters to ensure that all allow
-for questions to be answered correctly. Crowd-
-workers were shown four documents in succession,
-in a between-participants design. Order of docu-
-ment and stimuli type were randomized. Figure 6
-shows two stimuli of a document (original and KiS)
-along with the comprehension questions. (The en-
-tire set of five stimuli can be found in Figure A2 in
-the Appendix.)
-After several rounds of pilot testing, we arrived
-at the following design choices:
-Document theme. We chose recent news arti-
-cles involving complex themes (e.g., trajectory of
-iceberg) as the source of documents. For news ar-
-ticles, recency seems to engage participants, andORIGINAL [Lexile Grade 1 1] Each summer , libraries in St. Louis,
-Missouri, host many  types of free camps — yoga, chess and even a
-Harry Pot ter “Sorting Hat Camp.” In 2020, camp dreams seemed far-
-fetched given the global coronavirus pandemic. That didn’t stop St.
-Louis libraries, though.
-Instead of canceling, they brought camp into kids’ homes. So children
-who signed up for ukulele camp got a beginner ’s guidebook,
-instructional DVD and an actual ukulele in the mail. It was all free. In
-addition, camp sessions still occurred. Advisers met with kids using
-virtual formats.
-Joe Monahan, manager of youth services for the St. Louis  library
-system, says that of the 70 camps originally scheduled, 54 were held
-virtually .
-Paula Langsam, a youth services manager at the soon-to- reopen
-Martin Luther King Junior Memorial Library in W ashington, D.C., says,
-“In a way , our work has changed a lot. W e didn’t used to do videos a
-lot.”KIS MODEL [Lexile Grade 9] In the summer months, St. Louis
-has many free classes for  kids, including yoga, chess and a Harry
-Potter “Sorting Hat Camp.” In 2020, camp dreams again seemed
-far-fetched  given the crisis. That didn’t stop St. Louis libraries,
-though.
-They brought camp in. So kids who signed up for ukulele camp got
-a beginner ’s guidebook, a lesson DVD and a real ukulele in the
-mailbox. It was all free. In addition, camp  sessions continued.
-Advisers tried out a virtual format.
-Joe Monahan, the manager of youth services for the St. Louis
-library system, says that of the 70 camps originally scheduled, 54
-were held mostly .
-Paula Langsam, a youth services manager at the Martin Luther
-King Junior library , says, “In a way , our work changed a lot. W e
-didn’t do videos a lot.”
-Who manages the St Louis library kids programs?
-Joe Monahan , Paula Langsam, St. Louis Camp Leaders
-Were any camps in St. Louis cancelled?
-Yes, NoHow many camps were scheduled, how many were run?
-54 and 70, 70 and 54 , 70 and 0, 54 and 0
-How did the Ukulele camp meet?
-In the park, Virtually , Did not meetWhat camps did the libraries host?
-Yoga, Chess , Pottery , Ukulele
-Figure 6: Example Task (from a Washington Post article (Kelati, 2020)) for the Comprehension Study. Shown
-are two of five stimuli: original document (left), and KiS model output (right). Participants read a text and answered
-comprehension questions (bottom). Average completion time was 160 seconds (original) and 136 seconds (KiS
-model output).
-technical terms increase the impact of simplifica-
-tion.
-Section length. We chose document length of
-3-4 paragraphs (or 200 words), and five compre-
-hension questions. Document length should not be
-too W (makes some questions trivial), or too long
-(adds a retrieval component to the task).
-Selection of questions. Questions were gener-
-ated via a GPT2 question generation model fine-
-tuned on the NewsQA dataset (Trischler et al.,
-2017). We select questions answerable by both
-the original and Newsela references, attempting to
-have both factoid (answer is entity) and reasoning
-questions.
-Re-submission until correct. When submitting
-answers, participants received feedback on which
-were incorrect, and were required to re-submit un-
-til all answers were correct. This aligns the ob-
-jective of the participant (i.e., finishing the task
-rapidly), with the task’s objective (i.e., measuring
-participant’s efficiency at understanding). This also
-gives a way to discourage participants from “brute-
-forcing” the task, re-submitting many combinations
-until one works.
-We note that some components of the study such
-as the choice of document themes and the selection
-of comprehension questions are elements that cre-
-ate variability in the results. We release the models
-used in the study, as well all generated texts that
-were evaluated to enable follow-up research and to
-aid reproducibility.Model Time (sec) # Subs. Comp. CASpeed
-[Original 174.0 4.23 1.0 1.00
-\Newsela 163.3 5.10 1.08 1.15
-8ACCESS 188.5 6.69 0.96 0.88
-9Finetune Baseline 161.0 8 4.70 0.97 1.04
-rKiS Model 142.6[\84.108 0.87 1.06
-Table 2: Results of the Human Comprehension
-Study. We measure average completion time (Time),
-number of submissions (#Subs.), compression ra-
-tio (Comp.) and a compression-accounted speed-up
-(CASpeed). Each text version is assigned a symbol
-used to indicate statistical significance ( p<0:05).
-5.3.2 Study Results
-We ran the study on Mechanical Turk, accepting
-crowd-workers with 1700+ completed tasks, and
-an acceptance rate of 97%+. The study was active
-for two weeks in December 2020, and remunerated
-participants completing all four sections at a rate of
-$10/hour. (Appendix A.2 shows crowd-worker in-
-structions and the document/version distributions.)
-When removing “brute-forced” submissions (10+
-re-submissions), we are left with 244 submissions,
-used for result analysis reported in Table 2, (A more
-detailed results table is included in Appendix A.4.)
-We measure two outcomes: question comple-
-tion time (in seconds), and number of submissions
-to correctness. We performed a Kruskal-Wallis
-test (Kruskal and Wallis, 1952) with a Dunn post-
-hoc test (Dunn, 1964) for statistical significance
-between pairs of conditions.
-In line with study objectives, simplified textshelp participants complete the task faster than read-
-ing original texts, with three of the four simplified
-versions leading to improvements in completion
-times. Participants were fastest with KiS simpli-
-fications (18% faster). The KiS model led to a
-statistically significant speed-up compared to the
-originals, Newsela references, and ACCESS sim-
-plifications. ACCESS simplifications surprisingly
-led to a non-significant slow-down, which we at-
-tribute to a potential loss in fluency that might have
-confused participants.
-One important factor we consider is that shorter
-passages (i.e., smaller compression) might lead to a
-speed-up regardless of simplicity. We confirm this
-by finding a small positive correlation between pas-
-sage length and completion time of 0.09. We com-
-pute a compression-adjusted speed-up (CASpeed )
-ratio by: (1) computing the passage length of each
-simplified version, (2) linearly extrapolating the ex-
-pected completion time for this passage length for
-original paragraphs, and (3) computing the ratio of
-the extrapolation to the observed completion time.
-IfCASpeed> 1, participants were faster than ex-
-pected for the passage length. Newsela reference
-paragraphs achieve the best CASpeed , followed by
-the KiS model. This suggests that good simplifica-
-tion can involve making texts longer.
-5.4 Ablation Study
-We train three ablated models, each missing a re-
-ward component to gain understanding in the value
-of each component of the KiS procedure.
-Figure 1 gives a qualitative perspective on each
-ablation. Without fluency, the generator learns to
-generate incomplete sentences, without salience, it
-omits important information, and without simplic-
-ity, it can sometimes “complexify”.
-We computed complete automatic results for the
-ablated models, and find that each ablation leads to
-a decrease on an evaluation metric, confirming that
-all three components are necessary to generate high-
-quality simplifications (details in Appendix A.5).
-6 Limitations and Future Work
-Improved Accuracy Scoring . The current
-guardrail for inaccuracy is rudimentary; trained
-models still generate non-factual simplifications.
-Recent work in fact-checking for the summariza-
-tion domain (Kryscinski et al., 2020; Li et al., 2018)
-could be adapted to the simplification domain to
-improve this.Inclusion of Supervised Signal . In this work,
-we establish that text simplification can be ap-
-proached in an unsupervised manner. In future
-work, Keep it Simple could be used as a pre-
-training strategy, or used jointly with supervised
-training.
-Reproducibility of Human Evaluation . Even
-though we release the models, stimuli and compre-
-hension questions used in the human evaluation,
-some elements of the procedure introduce random-
-ness. Participating crowd-workers differ in literacy
-level which may have an effect on their perfor-
-mance at the task (Alonzo et al., 2021).
-New Settings, Domains and Languages . We
-limited our experiments to the simplification of En-
-glish news articles following prior work, but plan
-to pursue other languages in the future. Similarly,
-because Keep it Simple does not require labeled
-data, it can be applied to new settings (e.g., rewrit-
-ing to inverse the effects of simplification), or to
-new domains (e.g., legal texts).
-7 Conclusion
-We have shown that text simplification can be ap-
-proached in an unsupervised manner via KiS. By
-optimizing a reward comprised of simplicity, flu-
-ency and salience components, KiS is able to out-
-perform strong supervised models on automatic
-metrics (+0.04 in SARI). We propose a human
-comprehension task to evaluate the usefulness of
-simplification and show that simplifications tend to
-lead to a measurable speed-up in task completion,
-with KiS texts producing the best speed-up of 18%
-on average. These are first steps for unsupervised
-text simplification, and we suggest that future work
-should focus on adapting the methodology to new
-domains (i.e., legal), non-English languages, and
-refining optimized rewards to take factuality into
-account.
-Acknowledgments
-We would like to thank Katie Stasaski, Dongyeop
-Kang, and the ACL reviewers for their helpful com-
-ments, as well as Newsela for providing a version
-of their simplified news corpus. This work was
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-We present a method for text simplification and ver-
-ify its performance on text from the news domain
-in the English language. Even though we expect
-the method to be adaptable to other domains and
-languages, we have not verified this assumption
-experimentally and limit our claims to the English
-news domain.
-When comparing to prior work (e.g., ACCESS
-model), we obtained implementations directly from
-the authors (through Github repositories) and pro-
-duced results following the recommended setting,
-with an objective to present prior work as a strong
-comparison point.
-For the human evaluation, we paid the annota-
-tors above the minimum wage, and did not collect
-any personal identifiable information. We selected
-topics to avoid sensitive or political subjects and
-had our protocols reviewed by the university’s IRB
-committee (Protocol ID: 2018-07-11230). We re-
-lied on a third party (Amazon Mechanical Turk) to
-remunerate the crowd-workers.
-A Appendices
-A.1 Training Details
-We detail the model architecture size, data, opti-
-mizer of the models we train in the paper. All
-models were trained using Pytorch and Hugging-
-Face’s Transformers library5. We use the Apex6
-library to enable half-precision training.
-The KiS procedure was trained on a single
-GPU, either an Nvidia V-100 (16Gb memory) or
-a Quadro RTX 8000 (48 Gb memory). We ran a
-total of around 200 experiments, with an average
-run-time of one week.
-Because the procedure is unsupervised, the
-model was trained using a large unreleased cor-
-pus of news articles, containing 7 million news
-articles in English.
-KiS Model is initialized with a GPT2-medium
-model. We used the Adam optimizer, with a learn-
-ing rate of 106, a batch-size of 1, using k-SCST
-withk= 8.
-Finetune Baseline is initialized with a GPT2-
-medium model. We train using using standard
-teacher forcing on the 40,000 samples in the paired
-Newsela dataset , reserving 2,000 samples for val-
-idation. We use the Adam optimizer, and use the
-5https://github.com/huggingface/transformers
-6https://github.com/nvidia/apexvalidation set to choose a learning rate of 105,
-and a batch-size of 8, and run for 3 epochs before
-seeing a plateau in the validation loss.
-Discriminator Model is initialized with a
-Roberta-base , and retrained every time the train-
-ing buffer reaches 2,000 samples. The discrim-
-inator is reset to the original Roberta-base each
-time the training buffer is full. We use a standard
-cross-entropy loss, the ADAM optimizer with a
-learning rate of 105and a batch size of 8. Each
-time we retrain, we run for 5 epochs, and check-
-point one model after each epoch. The checkpoint
-that achieves the highest performance on a valida-
-tion set becomes the new discriminator for the next
-round.
-A.2 Human Evaluation Instructions
-Figure A1 shows the instructions given to crowd-
-worker participants for the manual evaluation.
-•The entire HIT should take no more than 15
-minutes:
-(1) You will answer a pre-questionnaire.
-(2) Read 4 short news stories and answer
-comprehension questions about each.
-•If you believe the answer is not in the
-document, you can select the option “Answer
-not in document”.
-•There is no time limit for each individual
-document or question.
-•You can leave at any point but will not
-complete the HIT.
-• You can complete this task at most once.
-•If you have a question/problem, contact us at
-email .
-Figure A1: Instructions given to participants of the
-comprehension evaluation. Participants were recruited
-on Amazon Mechanical Turk (MTurk), on which jobs
-are named “HIT”.
-A.3 Full Example of Generated Texts
-Figure A2 is a complement to Figure 6, with the
-five stimuli that were shown for the Covid Libraries
-document.
-A.4 Detailed of Human Evaluation Results
-Table A1 details the timing and number of par-
-ticipants for each combination of document and
-stimuli.ORIGINAL [Lexile Grade 1 1] Each summer , libraries in St. Louis, Missouri, host many  types of free camps — yoga, chess and even a
-Harry Pot ter “Sorting Hat Camp.” In 2020, camp dreams seemed far- fetched given the global coronavirus pandemic. That didn’t stop
-St. Louis libraries, though.
-Instead of canceling, they brought camp into kids’ homes. So children who signed up for ukulele camp got a beginner ’s guidebook,
-instructional DVD and an actual ukulele in the mail. It was all free. In addition, camp sessions still occurred. Advisers met with kids
-using virtual formats.
-Joe Monahan, manager of youth services for the St. Louis  library system, says that of the 70 camps originally scheduled, 54 were held
-virtually .
-Paula Langsam, a youth services manager at the soon-to- reopen Martin Luther King Junior Memorial Library in W ashington, D.C.,
-says, “In a way , our work has changed a lot. W e didn’t used to do videos a lot.”
-Who manages the St Louis library kids programs?
-Joe Monahan , Paula Langsam, St. Louis Camp Leaders
-Were any camps in St. Louis cancelled?
-Yes, NoHow many camps were scheduled, how many were run?
-54 and 70, 70 and 54 , 70 and 0, 54 and 0
-How did the Ukulele camp meet?
-In the park, Virtually , Did not meetWhat camps did the libraries host?
-Yoga, Chess , Pottery , UkuleleKIS MODEL [Lexile Grade 9] In the summer months, St. Louis has many free classes for  kids, including yoga, chess and a Harry
-Potter “Sorting Hat Camp.” In 2020, camp dreams again seemed far-fetched  given the crisis. That didn’t stop St. Louis libraries, though.
-They brought camp in. So kids who signed up for ukulele camp got a beginner ’s guidebook, a lesson DVD and a real ukulele in the
-mailbox. It was all free. In addition, camp  sessions continued. Advisers tried out a virtual format.
-Joe Monahan, the manager of youth services for the St. Louis library system, says that of the 70 camps originally scheduled, 54 were
-held mostly .
-Paula Langsam, a youth services manager at the Martin Luther King Junior library , says, “In a way , our work changed a lot. W e didn’t
-do videos a lot.”NEWSELA [Lexile Grade 7] In St. Louis, Missouri, libraries hold many types of free camps in the summer . They have yoga, chess and
-even a Harry Potter camp. In 2020, camp ideas seemed unlikely due to the spread of COVID-19. That did not stop St. Louis libraries,
-though. They did not cancel their programs. They brought camp into kids' homes.
-Children who signed up for ukulele camp got a beginner's book, instructional video and a ukulele in the mail. It was all free. In addition,
-camp sessions still went on. Camp leaders met with kids over video calls.
-Joe Monahan runs the kids programs for the St. Louis libraries. He said 70 camps were scheduled at first. Some were dropped but 54
-were still held online.
-Paula Langsam works at the Martin Luther King Junior Memorial Library in W ashington, D.C. It reopened on September 24. She said,
-"Our work has changed a lot. W e didn't used to do videos a lot."
-FINETUNE BASELINE [Lexile Grade 9] Each summer , St. Louis libraries host many types of free camps — yoga, chess and even a
-Harry Potter "Sorting Hat Camp." In 2020, camp dreams seemed far-fetched given the global cough . However , the future looked
-bright for libraries in St. Louis.
-Instead of canceling, they brought camp into kids' homes. This is called ukulele camp. Kids signed up for it in the mail. They got a
-beginner's guidebook, video and an actual ukulele to play . It was all free. Advisers met with kids using virtual formats.
-Joe Monahan, manager of youth services for the St. Louis library system, says that 70 camps were originally planned. Of those, 54
-were held mostly .
-Paula Langsam is a youth services manager who works at the Martin Luther King Junior Memorial Library in W ashington, D.C. She
-says, "In a way , our work has changed a lot. W e didn't used to do videos a lot."
-ACCESS [Lexile Grade 1 1] Each summer , libraries in St. Louis, Missouri, has many dif ferent types of free camps that are yoga, chess
-and even a Harry Potter gang Sorting Hat Camp. In 2020, camp dreams seemed far-fetched that there was the global coronavirus
-pandemic. That did not stop St. Louis libraries, though.
-Instead of being canceled, they brought camp into children's homes. So children who signed up for ukulele camp got a guidebook.
-They also had an actual ukulelele in the mail. It was all free. In addition, camp meetings still happened. Advisers met with new children
-using virtual formats.
-Joe Monahan, also known as Joe Monahan, has youth services for the St. Louis library system says that of the 70 camps first started,
-54 were held.
-Paula Langsam, also known as Paula Langsam, is a youth services manager at the soon-to-reopen Martin Luther King Junior Library in
-Washington, D. W e did not use to do many videos a lot.Figure A2: Complement to Figure 6. Example Task for the Comprehension Study. Participants were assigned
-to one of five settings: original, Newsela, KiS, Finetune Baseline, and ACCESS. Participants were instructed to
-answer the five comprehension questions.
-Simplification Model
-Document Id Original Newsela Sup. Base. ACCESS KiS
-Marvel Show 152 (12) 209 (11) 140 (11) 209 (14) 126 (13)
-Covid Libraries 167 (14) 180 (12) 182 (10) 190 (13) 171 (12)
-Sustainable Food 163 (13) 144 (10) 181 (13) 242 (13) 154 (12)
-Iceberg Collision 208 (14) 116 (11) 139 (12) 104 (12) 119 (12)
-Version Aggregate 174 (53) 163 (44) 161 (46) 188 (52) 143 (49)
-Table A1: Average time taken and number of participants in each of the document/stimuli combinations.
-Also shown are aggregates (mean time taken and total number of participants).Model SARI BLEU %FKGL %Lexile Comp. Cov.
-KiS Full 0.709 0.526 100 72 0.85 0.636
-KiS No Fluency 0.718 0.611 99 95 1.02 0.901
-KiS No Salience 0.695 0.591 100 65 1.01 0.701
-KiS No Simplicity 0.672 0.617 51 23 0.92 0.809
-Table A2: Automatic results of the three ablation models. SARI andBLEU are reference-based metrics. %
-FKGL and% Lexile are the percentage of simplified paragraphs with a lower FKGL and Lexile score than the
-original paragraph. Comp. is the average compression ratio (# of words), and Cov. is the average coverage score
-of the simplifications.
-A.5 Detail of Ablation Study Results
-Table A2 details the metric results of the three ab-
-lated models, an extension to Table 1. An example
-output of each ablated model, illustrating the limi-
-tation when a score component is missing, is given
-in Figure 1.
-One surprising element is that the model trained
-without fluency achieves higher scores on almost
-all metrics, compared to the full model. This sur-
-prising fact is due to the fact that without fluency,
-the model does not learn to generate full sentences
-(see the example in Figure 1). Instead, the model
-learns to concatenate high-scoring phrases together,
-which can boost automatic metrics artificially. In
-fact, the strong performance of a model generating
-incomplete sentences reveals a limitation of current
-automatic metrics, such as BLEU and SARI.

txt/2107.13662.txt

@@ -1,598 +0,0 @@
-Investigating Text Simplification Evaluation
-Laura V ´asquez-Rodr ´ıguez1,Matthew Shardlow2,Piotr Przybyła3,Sophia Ananiadou1
-1National Centre for Text Mining,
-The University of Manchester, Manchester, United Kingdom
-2Department of Computing and Mathematics,
-Manchester Metropolitan University, Manchester, United Kingdom
-3Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland
-flaura.vasquezrodriguez, sophia.ananiadou [email protected]
-[email protected] [email protected]
-Abstract
-Modern text simplification (TS) heavily relies
-on the availability of gold standard data to
-build machine learning models. However, ex-
-isting studies show that parallel TS corpora
-contain inaccurate simplifications and incor-
-rect alignments. Additionally, evaluation is
-usually performed by using metrics such as
-BLEU or SARI to compare system output to
-the gold standard. A major limitation is that
-these metrics do not match human judgements
-and the performance on different datasets and
-linguistic phenomena vary greatly. Further-
-more, our research shows that the test and
-training subsets of parallel datasets differ sig-
-nificantly. In this work, we investigate existing
-TS corpora, providing new insights that will
-motivate the improvement of existing state-of-
-the-art TS evaluation methods. Our contribu-
-tions include the analysis of TS corpora based
-on existing modifications used for simplifica-
-tion and an empirical study on TS models per-
-formance by using better-distributed datasets.
-We demonstrate that by improving the distribu-
-tion of TS datasets, we can build more robust
-TS models.
-1 Introduction
-Text Simplification transforms natural language
-from a complex to a simple format, with the aim to
-not only reach wider audiences (Rello et al., 2013;
-De Belder and Moens, 2010; Aluisio et al., 2010;
-Inui et al., 2003) but also as a preprocessing step in
-related tasks (Shardlow, 2014; Silveira and Branco,
-2012).
-Simplifications are achieved by using parallel
-datasets to train sequence-to-sequence text gen-
-eration algorithms (Nisioi et al., 2017) to make
-complex sentences easier to understand. They are
-typically produced by crowdsourcing (Xu et al.,
-2016; Alva-Manchego et al., 2020a) or by align-
-ment (Cao et al., 2020; Jiang et al., 2020). They areinfamously noisy and models trained on these give
-poor results when evaluated by humans (Cooper
-and Shardlow, 2020). In this paper we add to the
-growing narrative around the evaluation of natu-
-ral language generation (van der Lee et al., 2019;
-Caglayan et al., 2020; Pang, 2019), focusing on
-parallel text simplification datasets and how they
-can be improved.
-Why do we need to re-evaluate TS resources?
-In the last decade, TS research has relied on
-Wikipedia-based datasets (Zhang and Lapata, 2017;
-Xu et al., 2016; Jiang et al., 2020), despite their
-known limitations (Xu et al., 2015; Alva-Manchego
-et al., 2020a) such as questionable sentence pairs
-alignments, inaccurate simplifications and a limited
-variety of simplification modifications. Apart from
-affecting the reliability of models trained on these
-datasets, their low quality influences the evaluation
-relying on automatic metrics that requires gold-
-standard simplifications, such as SARI (Xu et al.,
-2016) and BLEU (Papineni et al., 2001).
-Hence, evaluation data resources must be further
-explored and improved to achieve reliable evalu-
-ation scenarios. There is a growing body of ev-
-idence (Xu et al., 2015) (including this work) to
-show that existing datasets do not contain accurate
-and well-constructed simplifications, significantly
-impeding the progress of the TS field.
-Furthermore, well-known evaluation metrics
-such as BLEU are not suitable for simplification
-evaluation. According to previous research (Sulem
-et al., 2018) BLEU does not significantly correlate
-with simplicity (Xu et al., 2016), making it inap-
-propriate for TS evaluation. Moreover, it does not
-correlate (or the correlation is low) with grammati-
-cality and meaning preservation when performing
-syntactic simplification such as sentence splitting.
-Therefore in most recent TS research BLEU has
-not been considered as a reliable evaluation metric.
-We use SARI as the preferred method for TS eval-arXiv:2107.13662v1  [cs.CL]  28 Jul 2021uation, which has also been used as the standard
-evaluation metric in all the corpora analysed in this
-research.
-Our contributions include 1) the analysis of the
-most common TS corpora based on quantifying
-modifications used for simplification, evidencing
-their limitations and 2) an empirical study on TS
-models performance by using better-distributed
-datasets. We demonstrate that by improving the
-distribution of TS datasets, we can build TS mod-
-els that gain a higher SARI score in our evaluation
-setting.
-2 Related Work
-The exploration of neural networks in TS started
-with the work of Nisioi et al. (2017), using
-the largest parallel simplification resource avail-
-able (Hwang et al., 2015). Neural-based work
-focused on state-of-the-art deep learning and
-MT-based methods, such as reinforcement learn-
-ing (Zhang and Lapata, 2017), adversarial train-
-ing (Surya et al., 2019), pointer-copy mecha-
-nism (Guo et al., 2018), neural semantic en-
-coders (Vu et al., 2018) and transformers supported
-by paraphrasing rules (Zhao et al., 2018).
-Other successful approaches include the usage
-of control tokens to tune the level of simplification
-expected (Alva-Manchego et al., 2020a; Scarton
-and Specia, 2018) and the prediction of operations
-using parallel corpora (Alva-Manchego et al., 2017;
-Dong et al., 2020). The neural methods are trained
-mostly on Wikipedia-based sets, varying in size
-and improvements in the quality of the alignments.
-Xu et al. (2015) carried out a systematic study on
-Wikipedia-based simplification resources, claim-
-ing Wikipedia is not a quality resource, based on
-the observed alignments and the type of simplifi-
-cations. Alva-Manchego et al. (2020a) proposed
-a new dataset, performing a detailed analysis in-
-cluding edit distance and proportion of words that
-are deleted, inserted and reordered, and evaluation
-metrics performance for their proposed corpus.
-Chasing the state-of-the-art is rife in NLP (Hou
-et al., 2019), and no less so in TS, where a SARI
-score is too often considered the main quality indi-
-cator. However, recent work has shown that these
-metrics are unreliable (Caglayan et al., 2020) and
-gains in performance according to them may not de-
-liver improvements in simplification performance
-when the text is presented to an end user.3 Simplification Datasets: Exploration
-3.1 Data and Methods
-In the initial exploration of TS datasets, we investi-
-gated the training, test and validation subsets (when
-available) of the following: WikiSmall and Wiki-
-Large (Zhang and Lapata, 2017), TurkCorpus (Xu
-et al., 2015), MSD dataset (Cao et al., 2020), AS-
-SET (Alva-Manchego et al., 2020a) and WikiMan-
-ual (Jiang et al., 2020). For the WikiManual dataset,
-we only considered sentences labelled as “aligned”.
-We computed the number of changes between
-the original and simplified sentences through the
-token edit distance . Traditionally, edit distance
-quantifies character-level changes from one char-
-acter string to another (additions, deletions and re-
-placements). In this work, we calculated the token-
-based edit distance by adapting the Wagner–Fischer
-algorithm (Wagner and Fischer, 1974) to determine
-changes at a token level. We preprocessed our
-sentences by changing them into lowercase prior
-to this analysis. To make the results comparable
-across sentences, we divide the number of changes
-by the length of the original sentence and obtain
-values between 0% (no changes) to 100% (com-
-pletely different sentence).
-In addition to toked-based edit operation exper-
-iments, we analysed the difference of sentence
-length between complex and simple variants, the
-quantity of edit operations type (INSERT, DELETE
-and REPLACE) and an analysis of redundant oper-
-ations such as deletions and insertions in the same
-sentence over the same text piece (we define this as
-the MOVE operation). Based on our objective to
-show how different split configurations affect TS
-model performance, we have presented the percent-
-age of edit operations as the more informative anal-
-ysis performed on the most representative datasets.
-3.2 Edit Distance Distribution
-Except for the recent work of Alva-Manchego et al.
-(2020b), there has been little work on new TS
-datasets. Most prior datasets are derived by align-
-ing English and Simple English Wikipedia, for ex-
-ample WikiSmall andWikiLarge (Zhang and La-
-pata, 2017).
-In Figure 1 we can see that the edit distance
-distribution of the splits in the selected datasets is
-not even. By comparing the test and development
-subsets in WikiSmall (Figure 1a) we can see dif-
-ferences in the number of modifications involved
-in simplification. Moreover, the WikiLarge dataset(a) WikiSmall Test/Dev/Train
- (b) WikiLarge Test/Dev/Train
- (c) TurkCorpus Test
-(d) MSD Test
- (e) ASSET Test
- (f) WikiManual Test/Dev/Train
-Figure 1: Comparison of TS datasets with respect to the number of edit operations between the original and
-simplified sentences. X-axis: token edit distance normalised by sentence length, Y-axis: probability density for the
-change percentage between complex and simple sentence pairs.
-(Figure 1b) shows a complete divergence of the test
-subset. Additionally, it is possible to notice a signif-
-icant number of unaligned or noisy cases, between
-the 80% and 100% of change in the WikiLarge
-training and validation subsets (Figure 1b).
-We manually checked a sample of these cases
-and confirmed they were poor-quality simplifica-
-tions, including incorrect alignments. The simplifi-
-cation outputs (complex/simple pairs) were sorted
-by their edit distances and then manually checked
-to determine an approximate heuristic for noisy sen-
-tences detection. Since many of these alignments
-had really poor quality, it was easy to determine the
-number that removed a significant number of cases
-without actually reducing dramatically the size of
-the dataset.
-Datasets such as Turk Corpus (Xu et al., 2015)
-are widely used for evaluation and their opera-
-tions mostly consist of lexical simplification (Alva-
-Manchego et al., 2020a). We can see this behaviour
-in Figure 1c, where most edits involve a small per-
-centage of the tokens. This can be noticed when a
-large proportion of the sample cases are between
-0% (no change) to 40%.
-In the search of better evaluation resources, Turk-
-Corpus was improved with the development of
-ASSET (Alva-Manchego et al., 2020a) including
-more heterogeneous modification measures. As
-we can see in Figure 1e, the data are more evenly
-distributed than in Figure 1c.Recently proposed datasets, such as WikiMan-
-ual(Jiang et al., 2020), as shown in Figure 1f, have
-an approximately consistent distribution, and their
-simplifications are less conservative. Based on a
-visual inspection on the uppermost values of the
-distribution (80%), we can tell that often most
-of the information in the original sentence is re-
-moved or the target simplification does not express
-accurately the original meaning.
-MSD dataset (Cao et al., 2020) is a domain-
-specific dataset, developed for style transfer in the
-health domain. In the style transfer setting, the
-simplifications are aggressive (i.e., not limited to
-individual words), to promote the detection of a
-difference between one style (expert language) and
-another (lay language). Figure 1d shows how their
-change-percentage distribution differs dramatically
-in comparison to the other datasets, placing most
-of the results at the right-side of the distribution.
-Among TS datasets, it is important to mention
-that the raw text of the Newsela (Xu et al., 2015)
-dataset was produced by professional writers and is
-likely of higher quality than other TS datasets. Un-
-fortunately, it is not aligned at the sentence level by
-default and its usage and distribution are limited by
-a restrictive data agreement. We have not included
-this dataset in our analysis due to the restrictive
-licence under which it is distributed.Dataset Split KL-div p-value
-WikiSmallTest/Dev 0.0696 0.51292
-Test/Tr 0.0580 0.83186
-WikiLargeTest/Dev 0.4623 <0.00001
-Test/Tr 0.4639 <0.00001
-WikiManualTest/Dev 0.1020 0.00003
-Test/Tr 0.0176 0.04184
-TurkCorpus Test/Dev 0.0071 0.00026
-ASSET Test/Dev 0.0491 <0.00001
-Table 1: KL-divergence between testing (Test) and de-
-velopment (Dev) or training (Tr) subsets.
-3.3 KL Divergence
-In addition to edit distance measurements presented
-in Figure 1, we further analysed KL divergence
-(Kullback and Leibler, 1951) of those distributions
-to understand how much dataset subsets diverge.
-Specifically, we compared the distribution of the
-test set to the development and training sets for
-WikiSmall, WikiLarge, WikiManual, TurkCorpus
-and ASSET Corpus (when available). We did not
-include MSD dataset since it only has a testing set.
-We performed randomised permutation
-tests (Morgan, 2006) to confirm the statistical
-significance of our results. Each dataset was
-joined together and split randomly for 100,000
-iterations. We then computed the p-value as a
-percentage of random splits that result in the KL
-value equal to or higher than the one observed in
-the data. Based on the p-value, we can decide
-whether the null hypothesis (i.e. that the original
-splits are truly random) can be accepted. We reject
-the hypothesis for p-value lower than 0.05. In
-Table 1 we show the computed KL-divergence and
-p-values. The p-values below 0.05 for WikiManual
-and WikiLarge confirm that these datasets do not
-follow a truly random distribution.
-4 Simplification Datasets: Experiments
-We carried out the following experiments to eval-
-uate the variability in performance of TS models
-caused by the issues described in Wiki-based data.
-4.1 Data and Methods
-For the proposed experiments, we used the
-EditNTS model, a Programmer-Interpreter
-Model (Dong et al., 2020). Although the original
-code was published, its implementation required
-minor modifications to run in our setting. The
-modifications performed, the experimental subsetsas well as the source code are documented via
-GitHub1. We selected EditNTS model due to its
-competitive performance in both WikiSmall and
-WikiLarge datasets2. Hence, we consider this
-model as a suitable candidate for evaluating the
-different limitations of TS datasets. In future work,
-we will definitely consider testing our assumptions
-under additional metrics and models.
-In relation to TS datasets, we trained our mod-
-els on the training and development subsets from
-WikiLarge and WikiSmall, widely used in most
-of TS research. In addition, these datasets have
-a train, development and test set, which is essen-
-tial for retraining and testing the model with new
-split configurations. The model was first trained
-with the original splits, and then with the following
-variations:
-Randomised split : as explained in Section 3.3,
-the original WikiLarge split does not have an even
-distribution of edit-distance pairs between subsets.
-For this experiment, we resampled two of our
-datasets (WikiSmall and WikiLarge). For each
-dataset, we joined all subsets together and per-
-formed a new random split.
-Refined and randomised split : we created sub-
-sets that minimise the impact of poor alignments.
-These alignments were selected by edit distance
-and then subsets were randomised as above. We
-presume that the high-distance cases correspond
-to noisy and misaligned sentences. For both Wik-
-iSmall and WikiLarge, we reran our experiments
-removing 5% and 2% of the worst alignments.
-Finally, we evaluated the models by using the
-test subsets of external datasets, including: Turk-
-Corpus, ASSET and WikiManual.
-5 Discussion
-Figure 2 shows the results for WikiSmall. We can
-see a minor decrease in SARI score with the ran-
-dom splits, which means that the noisy alignments
-were equivalently present in all the sets rather than
-using the best cases for training. On the other hand,
-when the noisy cases are removed from the datasets
-the increase in model performance is clear.
-Likewise, we show WikiLarge results in Figure
-3. When the data is randomly distributed, we obtain
-better performance than the original splits. This
-1https://github.com/lmvasque/
-ts-explore
-2https://github.com/sebastianruder/
-NLP-progress/blob/master/english/
-simplification.mdFigure 2: SARI scores for evaluating WikiSmall-based
-models on external test sets.
-is consistent with WikiLarge having the largest
-discrepancy according to our KL-divergence mea-
-surements, as shown in Section 3.3. We also found
-that the 95% split gave a similar behaviour to Wiki-
-Large Random. Meanwhile, the 98% dataset, gave
-a similar performance to the original splits for AS-
-SET and TurkCorpus3.
-We can also note, that although there is a per-
-formance difference between WikiSmall Random
-and WikiSmall 95%, in WikiLarge the same splits
-have quite similar results. We believe these dis-
-crepancies are related to the size and distribution
-of the training sets. WikiLarge subset is three
-times bigger than WikiSmall in the number of sim-
-ple/complex pairs. Also, WikiLarge has a higher
-KL-divergence (0.46) than WikiSmall ( 0.06),
-which means that WikiLarge could benefit more
-from a random distribution experiment than Wik-
-iSmall, resulting in higher performance on Wiki-
-Large. Further differences may be caused by the
-procedures used to make the training/test splits in
-the original research, which were not described in
-the accompanying publications.
-Using randomised permutation testing, we have
-confirmed that the SARI differences between the
-models based on the original split and our best
-alternative (95% refined) is statistically significant
-(p <0:05) for each configuration discussed above.
-In this study, we have shown the limitations of
-TS datasets and the variations in performance in
-different splits configurations. In contrast, exist-
-ing evidence cannot determine which is the most
-suitable split, especially since this could depend
-on each specific scenario or target audience (e.g.,
-model data similar to “real world” applications).
-3ASSET and Turk Corpus results are an average on their
-multiple references scores.
-Figure 3: SARI scores for evaluating WikiLarge-based
-models on external test sets.
-Also, we have measured our results using SARI,
-not only because it is the standard evaluation metric
-in TS but also because there is no better automatic
-alternatives to measure simplicity. We use SARI
-as a way to expose and quantify SOTA TS datasets
-limitations. The increase in SARI scores should be
-interpreted as the variability in the relative quality
-of the output simplifications. By relative we mean,
-that there is a change in simplicity gain but we
-cannot state the simplification is at its best quality
-since the metric itself has its own weaknesses.
-6 Conclusions
-In this paper, we have shown 1) the statistical limita-
-tions of TS datasets, and 2) the relevance of subset
-distribution for building more robust models. To
-our knowledge, distribution-based TS datasets anal-
-ysis has not been considered before. We hope that
-the exposure of these limitations kicks off a discus-
-sion in the TS community on whether we are in the
-correct direction regarding evaluation resources in
-TS and more widely in NLG. The creation of new
-resources is expensive and complex, however, we
-have shown that current resources can be refined,
-motivating future studies in the field of TS.
-Acknowledgments
-We would like to thank Nhung T.H. Nguyen and
-Jake Vasilakes for their valuable discussions and
-comments. Laura V ´asquez-Rodr ´ıguez’s work was
-funded by the Kilburn Scholarship from the Uni-
-versity of Manchester . Piotr Przybyła’s work was
-supported by the Polish National Agency for Aca-
-demic Exchange through a Polish Returns grant
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-What Does TERRA-REF’s High Resolution, Multi Sensor Plant Sensing Public
-Domain Data Offer the Computer Vision Community?
-David LeBauer
-University of Arizona
-[email protected] Burnette
-University of Illinois
-[email protected] Fahlgren
-Donald Danforth Plant Science Center
-[email protected]
-Rob Kooper
-University of Illinois
-[email protected] McHenry
-University of Illinois
-[email protected] Stylianou
-St. Louis University
-[email protected]
-Abstract
-A core objective of the TERRA-REF project was to gen-
-erate an open-access reference dataset for the evaluation
-of sensing technologies to study plants under field condi-
-tions. The TERRA-REF program deployed a suite of high-
-resolution, cutting edge technology sensors on a gantry sys-
-tem with the aim of scanning 1 hectare (104m) at around 1
-mm2spatial resolution multiple times per week. The system
-contains co-located sensors including a stereo-pair RGB
-camera, a thermal imager, a laser scanner to capture 3D
-structure, and two hyperspectral cameras covering wave-
-lengths of 300-2500nm. This sensor data is provided along-
-side over sixty types of traditional plant phenotype measure-
-ments that can be used to train new machine learning mod-
-els. Associated weather and environmental measurements,
-information about agronomic management and experimen-
-tal design, and the genomic sequences of hundreds of plant
-varieties have been collected and are available alongside
-the sensor and plant phenotype data.
-Over the course of four years and ten growing seasons,
-the TERRA-REF system generated over 1 PB of sensor data
-and almost 45 million files. The subset that has been re-
-leased to the public domain accounts for two seasons and
-about half of the total data volume. This provides an un-
-precedented opportunity for investigations far beyond the
-core biological scope of the project.
-The focus of this paper is to provide the Computer Vi-
-sion and Machine Learning communities an overview of the
-available data and some potential applications of this one
-of a kind data.1. Introduction
-In 2015, the Advanced Research Projects Agency for En-
-ergy (ARPA-E) funded the TERRA-REF Phenotyping Plat-
-form (Figure 1). The scientific aim was to transform plant
-breeding by providing a reference dataset generated by de-
-ploying a suite of co-located high-resolution sensors un-
-der field conditions. The goal of these sensors was to use
-proximate sensing from approximately 2m above the plant
-canopy to quantify plant characteristics.
-The study has evaluated diverse populations of sorghum,
-wheat, and lettuce over the course of four years and ten
-cropping cycles. Future releases of additional data will be
-informed by user interests.
-Figure 1. TERRA-REF field scanner at the University of Arizona’s
-Maricopa Agricultural Center.
-The TERRA-REF reference dataset can be used to char-
-acterize phenotype-to-genotype associations, on a genomic
-scale, that will enable knowledge-driven breeding and the
-development of higher-yielding cultivars of sorghum and
-wheat. The data is also being used to develop new algo-
-rithms for machine learning, image analysis, genomics, and
-optical sensor engineering. Beyond applications in plantbreeding, the resulting dataset provides opportunities for the
-study and integration of diverse remote sensing modalities.
-1.1. Types of Data
-The TERRA-REF field scanner platform utilizes a sensor
-suite of co-located instruments (Figure 2 and Table 1). The
-TERRA-REF reference dataset includes several data types
-(Figures 3 and 4, Table 2) including raw and processed
-outputs from sensors, environmental sensor measurements,
-manually measured and computationally derived pheno-
-types, and raw and processed genomics datasets [16]. Ex-
-tensive contextual measurements and metadata include sen-
-sor information and extensive documentation for each of the
-sensors, the field scanner, calibration targets, and the results
-of sensor validation tests [16].
-In addition to raw sensor data, the first release of
-TERRA-REF data includes derived sensor data products in
-enhanced formats including calibrated and georeferenced
-images and point clouds (Table 2). Many of the data prod-
-ucts are provided in formats that follow Open Geospatial
-Consortium (OGC) standards and work with GIS software.
-Figure 2. TERRA-REF field scanner sensor suite.
-1.2. Sensors
-Sensors available on the TERRA-REF field scanner in-
-clude snapshot and line-scan imaging, multi-spectral radio-
-metric, and environmental sensors. Table 1 and Figure 2)
-provide a high level overview of the sensors deployed on
-this system. Full documentation and metadata for each sen-
-sor as well as the configuration and geometry of the sensor
-box are provided as metadata alongside the TERRA-REF
-data release.
-2. Computer Vision and Machine Learning
-Problems
-There are a variety of questions that the TERRA-REF
-dataset could be used to answer that are of high importance
-to the agricultural and plant science communities, while
-also posing extremely interesting and challenging computer
-vision and machine learning problems. In this section, weconsider example research areas or topics within computer
-vision and discuss the relevant agricultural and plant sci-
-ence questions those research communities could help ad-
-dress using the TERRA-REF data.
-Measurement, Prediction and Causal Inference. The
-TERRA-REF sensor data can be used to drive development
-of vision-based algorithms for fundamental problems in
-plant phenotyping, such as making measurements of plant
-height, leaf length, flower counting, or estimating environ-
-mental stress. Additional challenges include attempting to
-predict end of season phenotypes, such as end of season
-yield, from early season sensor data – an accurate predic-
-tor of end of season yield from early season visual data, for
-example, could help growers and breeders invest resources
-only in the most promising of candidate crops. There are
-additional opportunities to investigate the causal relation-
-ship between genotypes or environmental conditions and
-their expressed phenotypes, as the TERRA-REF dataset in-
-cludes both comprehensive genetic information, as well as
-high temporal resolution environmental information. The
-TERRA-REF data contain over sixty hand measurements
-that could be used to train models from one or more sensors.
-In addition, there are opportunities to train models that pre-
-dict plot-level phenotypes measured by an expensive sensor
-with a less expensive sensor. Further, many events includ-
-ing insect damage, heat stress, and plant lodging (falling)
-could be labeled in new images.
-Fine Grained Visual Categorization. The TERRA-REF
-data is a rich source of visual sensor data collected from
-crop species that are visually similar. Differentiating be-
-tween data with low inter-class variance is an interesting
-categorization challenge, requiring visual models that learn
-the fine-grained differences between varieties of the same
-crop.
-Transfer Learning. There are a variety of interesting
-transfer learning challenges of utmost importance to the
-agricultural and plant science communities, including dis-
-covering approaches that generalize across sensors, across
-crops, or across environmental conditions. The TERRA-
-REF data additionally presents an opportunity to help solve
-the greenhouse-to-field gap, where models that perform
-well in greenhouse conditions tend to not generalize to field
-conditions; because the TERRA-REF data includes both
-greenhouse and field data for the exact same varieties, re-
-searchers in transfer learning could help build models that
-bridge this gap.
-Multi-sensor Integration. The TERRA-REF data in-
-cludes data captured from a variety of visual sensors (de-
-scribed in Section 1.2). These sensors have similar, but notTable 1. Summary of TERRA-REF sensor instruments.
-Sensor Name Model Technical Specifications
-Imaging Sensors
-Stereo RGB Camera Allied Vision Prosilica GT3300C
-Laser Scanner Custom Fraunhofer 3D Spatial Resolution: 0.3 to 0.9 mm
-Thermal Infrared FLIR A615 Thermal Sensitivity: ≤50mK @ 30◦C
-PS II Camera LemnaTec PS II Fluorescence Prototype Illumination 635nm x 4000 µmol/m2/s, Camera 50 fps
-Multi-spectral Radiometers
-Dedicated NDVI Multispectral Radiometer Skye Instruments SKR 1860D/A 650 nm, 800 nm ±5 nm; 1 down, 1 up
-Dedicated PRI Multispectral Radiometer Skye Instruments SKR 1860ND/A 531nm +/- 3nm; PRI = Photochemical Reflectance Index
-Active Reflectance Holland Scientific Crop Circle ACS-430 670 nm, 730 nm, 780 nm
-Hyper-spectral Cameras
-VNIR Hyperspectral Imager Headwall Inspector VNIR 380-1000 nm @ 2/3 nm resolution
-SWIR Hyperspectral Imager Headwall Inspector SWIR 900-2500 nm @ 12 nm resolution
-Environmental Sensors
-Climate Sensors Thies Clima 4.9200.00.000
-VNIR Spectroradiometer Ocean Optics STS-Vis Range: 337-824 nm @ 1/2 nm
-VNIR+SWIR Spectroradiometer Spectral Evolution PSR+3500 Range 800-2500nm @3-8 nm; Installed 2018
-PAR Sensor Quantum SQ–300 Spectral Range 410 to 655 nm
-Table 2. Summary of the sensor data products included in the first release of TERRA-REF data.
-Data Product Sensor Algorithm File Format Plot Clip Full Field
-Environment Thies Clima envlog2netcdf netcdf NA NA
-Thermal Image FLIR ir geotiff geotiff +
-Point Cloud Fraunhofer Laser 3D laser3d las las +
-Point Cloud Fraunhofer Laser 3D scanner3DTop ply
-Images Time-Series PSII Camera ps2png png
-Color Images RGB Stereo bin2tiff geotiff + +
-Plant Mask RGB Stereo rgb mask geotiff x
-identical, viewpoints from within the gantry box, may not
-have captured data at the exact same time, and may have
-captured different perspectives of the same part of the field
-on different days. This presents interesting challenges in
-terms of how to incorporate information across the various
-sensors, and how to work with time-series data that is not
-necessarily well-aligned or continuously captured.
-Explainable Models. All too often in machine learning
-research, datasets and models are built solely to drive the
-development of machine learning algorithms. When build-
-ing models to answer questions like “should I cut this plant
-down because it won’t produce sufficient yield?” or “is this
-plant under environmental stress?,” it is important not just
-to have maximally accurate models but to also understand
-why the models make the determinations that they make.
-This makes the TERRA-REF data, and the biologically rel-
-evant questions it supports, an excellent opportunity to drive
-development of new approaches for explainable machine
-learning, conveying the decisions made by algorithms to
-non-machine learning experts.
-Information Content. The TERRA-REF field scanner
-and sensors represent a substantial investment, and it is still
-not clear which sensors, sensor configurations, and spatialand temporal resolutions are useful to answer a particular
-question. Presently, much less expensive sensors and sens-
-ing platforms are available [11, 1]. What do we gain from
-the 1mm spatial resolution on this platform relative to unoc-
-cupied aerial systems (UAS) that are quickly approaching
-1cm spatial resolution? Or, which subset of hyperspectral
-wavelengths provide the most useful information? Can we
-predict the useful parts of a hyperspectral image from RGB
-images? Or get most of the information from a multispec-
-tral camera with a half-dozen bands? At the outset, the team
-recognized that this configuration would be oversampling
-the plant subjects, but it wasn’t clear what the appropriate
-resolutions or most useful sensors would be.
-Overall Challenges. Within all of these topic areas in
-computer vision and machine learning, the challenges that
-must be addressed require addressing interesting questions
-such as determining the most appropriate sensors and data
-processing choices for specific questions, addressing dif-
-ficult domain transfer issues, considering how to integrate
-noisy side channels of information, such as genetic infor-
-mation that may conflict or conflate with each other, or deal-
-ing with nuisance parameters like environmental or weather
-variations that simultaneously influence plant subjects and
-the sensor data content.Figure 3. Example data from the TERRA-REF gantry system. (top left) RGB data (center top) RGB data with soil masked (top right)
-close up of RGB data. 3D-scanner data (center row, right to left) depth, reflectance, surface normals, and point cloud data produced by
-the 3D scanner. (bottom row, right to left) FLIR thermal image with transpiring leaves shown as cooler than soil; F v/Fmderived from
-active fluorescence PSII sensor providing a measure of photosynthetic efficiency; the reflectance of light at 543nm wavelength measured
-by the VNIR hyperspectral camera. Because these are two-dimensional representations of three-dimensional systems, all scale bars are
-approximate.
-3. Algorithm Development.
-The process of converting raw sensor outputs into usable
-data products required geometric, radiometric, and geospa-
-tial calibration. In this regard, each sensor presented its own
-challenges. Combining these steps into an automated com-
-puting pipeline also represented a substantial effort that is
-described by Burnette et al. [3].
-Radiometric calibration was particularly challenging,
-owing that many images contain both sunlit and shaded ar-
-eas. In the case of hyperspectral images, the white sensor
-box and scans spread out over multiple days confounded
-an already challenging problem. Radiometric calibration
-of images taken by the two hyperspectral cameras exempli-
-fies these challenges, and a robust solution is described by
-Sagan et al. [21] and implemented in [19]. Even process-
-ing images from an RGB camera was challenging due to
-fixed settings resulting in high variability in quality and ex-
-posure, requiring the novel approach described by Li et al.
-[18]. Herritt et al. [14, 13] demonstrate and provide soft-
-ware used in analysis of a sequence of images that capture
-plant fluorescence response to a pulse of light.Most of the algorithms used to generate data products
-have not been published as papers but are made available
-on GitHub ( https://github.com/terraref ); code
-used to release the data publication in 2020 is available on
-Zenodo [25, 15, 10, 6, 4, 19, 8, 7, 5, 9, 17].
-Pipeline development continues to support ongoing use
-of the field scanner as well as more general applica-
-tions in plant sensing pipelines. Recent advances have
-improved pipeline scalability and modularity by adopt-
-ing workflow tools and making use of heterogeneous
-computing environments. The TERRA-REF computing
-pipeline has been adapted and extended for continuing
-use with the Field Scanner with the new name ”Phy-
-toOracle” and is available at https://github.com/
-LyonsLab/PhytoOracle . Related work generalizing
-the pipeline for other phenomics applications has been re-
-leased under the name ”AgPipeline” https://github.
-com/agpipeline with applications to aerial imaging de-
-scribed by Schnaufer et al . [22]. All of these software
-are made available with permissive open source licenses on
-GitHub to enable access and community development.Figure 4. Summary of public sensor datasets from Seasons 4 and 6. Each dot represents the dates for which a particular data product is
-available, and the size of the dot indicates the number of files available.
-4. Uses to date.
-TERRA-REF is being used in a variety of ways. For ex-
-ample, hyperspectral images have been used to measure soil
-moisture [2], but the potential to predict leaf chemical com-
-position and biophysical traits related to photosynthesis and
-water use are particularly promising based on prior work
-[23, 24].
-Plant Science and Computer Vision Research. A few
-projects are developing curated datasets for specific ma-
-chine learning challenges related to classification, objectrecognition, and prediction.
-We currently know of at least three datasets curated
-for CVPPA 2021. The Sorghum-100 dataset was cre-
-ated to support development of algorithms that can clas-
-sify sorghum varieties from RGB images from Ren et al.
-[20]. Another set of RGB images curated for the Sorghum
-Biomass Prediction Challenge on Kaggle was developed
-with the goal of developing methods to predict end of sea-
-son biomass from images taken of different sorghum geno-
-types over the course of the growing season. Finally, RGB
-images from the TERRA-REF field scanner in Maricopa
-accounted for 250 of the 6000 1024x1024 pixel images inthe Global Wheat Head Dataset 2021 [12]. The goal of the
-Global Wheat Challenge 2021 on AIcrowd is to develop an
-algorithm that can identify wheat heads from a collection of
-images from around the world that represent diverse fields
-conditions, sensors, settings, varieties, and growth stages.
-Most of the research applications to date have focused on
-analysis of plot-level phenotypes and genomic data rather
-than the full resolution sensor data.
-5. Data Access
-Public Domain Data. A curated subset of the TERRA-
-REF data was released to the public domain in 2020 (Figure
-4) [16]. These data are intended to be re-used and are acces-
-sible as a combination of files and databases linked by spa-
-tial, temporal, and genomic information. In addition to pro-
-viding open access data, the entire computational pipeline is
-open source, and we can assist academic users with access
-to high-performance computing environments.
-The total size of raw (Level 0) data generated by these
-sensors is 60 TB. Combined, the Level 1 and Level 2 sen-
-sor data products are 490 TB. This size could be substan-
-tially reduced through compression and removal of dupli-
-cate data. For example, the same images at the same resolu-
-tion appear in the georeferenced Level 1 files, the full field
-mosaics, and the plot-level clip.
-Other Data Available. The complete TERRA-REF
-dataset is not publicly available because of the effort and
-cost of processing, reviewing, curating, describing, and
-hosting the data. Instead, we focused on an initial public
-release and plan to make new datasets available based on
-need. Access to unpublished data can be requested from
-the authors, and as data are curated they will be added to
-subsequent versions of the public domain release ( https:
-//terraref.org/data/access-data ).
-In addition to hosting an archival copy of data on Dryad
-[16], the documentation includes instructions for browsing
-and accessing these data through a variety of online portals.
-These portals provide access to web user interfaces as well
-as databases, APIs, and R and Python clients. In some cases
-it will be easier to access data through these portals using
-web interfaces and software libraries.
-The public domain data is archived on Dryad, with the
-exception of the large sensor data files. The Dryad archive
-provides a catalog of these files that can be accessed via
-Globus or directly on the host computer at the National Cen-
-ter for Supercomputing Applications.
-6. Acknowledgements
-The work presented herein was funded in part by the
-Advanced Research Projects Agency-Energy (ARPA-E),U.S. Department of Energy, under Award Numbers DE-
-AR0000598 and DE-AR0001101, and the National Science
-Foundation, under Award Numbers 1835834 and 1835543.
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txt/2108.06156.txt

@@ -1,1272 +0,0 @@
-EEEA-N ET: ANEARLY EXITEVOLUTIONARY NEURAL
-ARCHITECTURE SEARCH
-PUBLISHED AT ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE . DOI:
-https://doi.org/10.1016/j.engappai.2021.104397
-Chakkrit Termritthikun
-STEM, University of South Australia
-Adelaide, SA, 5095, Australia
-[email protected] Jamtsho
-College of Science and Technology
-Royal University of Bhutan
-Phuentsholing, 21101, Bhutan
-[email protected]
-Jirarat Ieamsaard
-Department of Electrical and Computer Engineering
-Faculty of Engineering, Naresuan University
-Phitsanulok, 65000, Thailand
-[email protected] Muneesawang
-Department of Electrical and Computer Engineering
-Faculty of Engineering, Naresuan University
-Phitsanulok, 65000, Thailand
-[email protected]
-Ivan Lee
-STEM, University of South Australia
-Adelaide, SA, 5095, Australia
-[email protected]
-ABSTRACT
-The goals of this research were to search for Convolutional Neural Network (CNN) architectures,
-suitable for an on-device processor with limited computing resources, performing at substantially
-lower Network Architecture Search (NAS) costs. A new algorithm entitled an Early Exit Population
-Initialisation (EE-PI) for Evolutionary Algorithm (EA) was developed to achieve both goals. The
-EE-PI reduces the total number of parameters in the search process by filtering the models with fewer
-parameters than the maximum threshold. It will look for a new model to replace those models with
-parameters more than the threshold. Thereby, reducing the number of parameters, memory usage
-for model storage and processing time while maintaining the same performance or accuracy. The
-search time was reduced to 0.52 GPU day. This is a huge and significant achievement compared to
-the NAS of 4 GPU days achieved using NSGA-Net, 3,150 GPU days by the AmoebaNet model, and
-the 2,000 GPU days by the NASNet model. As well, Early Exit Evolutionary Algorithm networks
-(EEEA-Nets) yield network architectures with minimal error and computational cost suitable for a
-given dataset as a class of network algorithms. Using EEEA-Net on CIFAR-10, CIFAR-100, and
-ImageNet datasets, our experiments showed that EEEA-Net achieved the lowest error rate among
-state-of-the-art NAS models, with 2.46% for CIFAR-10, 15.02% for CIFAR-100, and 23.8% for
-ImageNet dataset. Further, we implemented this image recognition architecture for other tasks, such
-as object detection, semantic segmentation, and keypoint detection tasks, and, in our experiments,
-EEEA-Net-C2 outperformed MobileNet-V3 on all of these various tasks. (The algorithm code is
-available at https://github.com/chakkritte/EEEA-Net ).
-Keywords Deep learning
Neural Architecture Search 
Multi-Objective Evolutionary Algorithms 
Image classification
-This work is done when Chakkrit Termritthikun works as a visiting research student in University of South AustraliaarXiv:2108.06156v1  [cs.CV]  13 Aug 2021EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-1 Introduction
-Deep convolutional neural networks (CNNs) have been widely used in computer vision applications, including image
-recognition, image detection, and image segmentation. In the ImageNet Large Scale Visual Recognition Challenge
-(ILSVRC) Russakovsky et al. [2015], the AlexNet Krizhevsky et al. [2017], GoogLeNet Szegedy et al. [2015], ResNet
-He et al. [2016], and SENet Hu et al. [2018] were represented models that had been widely used in various applications.
-The SqueezeNet Iandola et al. [2016], MobileNets Howard et al. [2017], NUF-Net Termritthikun et al. [2019, 2020],
-and ShuffleNet Zhang et al. [2018] models were simultaneously developed to be used on devices with limited resources.
-All of these architecture networks have been strengthened and advanced by developers for many years.
-However, a significant drawback of the useability of these models, and to the development of the efficient CNNs models,
-was the dependence on the designer’s expertise and experience, including utilising resources such as high-performance
-computing (HPC) for the experimentation. The datasets used for analysis also affect the model efficiency, depending on
-the different features that different datasets manifest, and all image recognition datasets require specialised research
-knowledge for each dataset when modelling. One algorithm, the NAS Zoph and Le [2016] was designed to search the
-CNN network architecture for different datasets and thereby avoided the hitherto human intervention or design activity
-except during the definition of the initial hyper-parameters.
-The CNN network architecture is flexible, allowing the model to be developed in different structures, with the module
-structure consisting of layers, linked in sequence with different parameters. Models obtained from NAS methods differ
-in structure and parameters, making NAS searches more efficient in finding dataset models, resulting in finding a model
-for each particular dataset which have a unique structure and parameter set.
-Reinforcement Learning (RL) and gradient descent algorithms, automate the search for models of deep learning.
-Searching a model with an RL algorithm takes 2,000 GPU days to uncover an effective model. However, when the
-gradient descent algorithm is focused on searching a model with the highest accuracy for only one objective; it takes
-4 GPU days. Both algorithms have difficulty in dealing with a multi-objective problem. EA, however, can solve
-multi-objective optimisation problems.
-EA apply optimisation methods that mimic the evolution of living organisms in nature, including reproduction, mutation,
-recombination, and selection. EA can find the most suitable candidate solution with quality function rates. EA-based
-NAS approaches are very robust with shallow error values for experiments with CIFAR-10 and CIFAR-100 datasets.
-However, past search algorithms in models of the EA-based NAS approaches have taken up to 3,150 GPU days in Real
-et al. [2019], 300 GPU days in Liu et al. [2018a], and 4 GPU days in Liu et al. [2018b]. Many network architectures
-built from NAS have a high number of parameters and high computing costs. It is obvious that extensive network
-architectures must be avoided when attempting to identify a network architecture that is suitable for other applications.
-A network architecture, built from the DARTS Liu et al. [2018b] search space, is a multi-path NAS. However, excessive
-path-level selection is a problem for multi-path NASs where every operation of the super network of a multi-path NAS
-takes a separate path. This means that all connected weights across all paths require considerable memory.
-The single-path NAS Stamoulis et al. [2019] was introduced to solve the NAS problem by finding a subset of kernel
-weights in the single layer. This layer is called a super kernel. We called the model built from the super kernel, the
-Supernet. Based on the Supernet, which has many subnets, whole subnets are trained at the same time through weight
-sharing. The Supernet can be sampled and deployed without re-training.
-In our current work, we developed the EE-PI method for Evolutionary NASs. EE-PI was applied to the Multi-Objective
-Evolutionary Algorithm (MOEA) in the first generation to locate the newly created model with a certain number of
-parameters, discarding models with parameters more than the set criteria. This process iteratively continues until the
-model with fewer parameters than the initial set criteria is found. Thus, EE-PI complements MOEA. We created this
-add-on to prevent models with a high number of parameters and a high number of Floating point Operations Per Second
-(FLOPS). Also, using a small number of parameters helps reduce model search costs.
-The key contributions of this paper are:
-•We introduce Evolutionary Neural Architecture Search (EA-Net), which adopts a multi-objective evolutionary
-algorithm for neural architecture search with three objectives: minimisation of errors, minimal number of
-parameters, and lowest computing costs.
-•We proposed a simple method called Early Exit Population Initialisation (EE-PI) to avoid a model with a high
-number of parameters and high computational cost, by filtering the neural network architecture based on the
-number of parameters in the first-generation of the evolution. The architectures obtained by this method are
-called Early Exit Evolutionary Algorithm networks (EEEA-Net).
-2EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Image 
-Softmax x N 
-x N 
-x N Normal 
-Normal Normal 
-Normal X1 X2 Image 
-Initial channel 
-X1 
-X1 
-X1 X2 
-X2 
-X2 Channel 
-increment 
-Reduction 
-Normal Normal 
-Reduction Normal 
-Figure 1: NASNet Zoph and Le [2016] network architecture (left) and NSGA-Net Lu et al. [2019] network architecture
-(right).
-•We conduct extensive experiments to evaluate the effectiveness of an EEEA-Net by outperforming MobileNet-
-V3 for all of the image recognition, object detection, semantic segmentation, and keypoint detection. Also,
-the EEEA-Net was widely tested on standard CIFAR-10 Krizhevsky [2009], CIFAR-100 Krizhevsky [2009],
-ImageNet Russakovsky et al. [2015], PASCAL VOC Everingham et al. [2010], Cityscapes Cordts et al. [2016],
-and MS COCO Lin et al. [2014] datasets.
-2 Related Work
-NAS was designed to search and design the model structure that suits the best to the applied dataset. Thus, the model
-obtained by NAS has a small number of parameters with high performance. NAS can find models suitable for both
-small and large datasets. The NAS can be of single-objective NAS and multi-objective NAS: a single-objective NAS
-considers models from a single objective such as error rate, number of parameters, or FLOPS. The multi-objective NAS
-considers models considering more than one objective, which we have adopted in this paper to optimise the model
-performance.
-2.1 Network Architecture Search
-Setting optimised parameters in each layer, such as kernel size, kernel scroll position (stride), zero paddings, as well as
-the output size, is the main challenge in creating CNN architectures efficiently for a given dataset. The total parameters
-are directly proportional to the number of layers. Manually designing a model takes too long and requires considerable
-experimentation to achieve optimal performance, which is why an automated model discovery is essential.
-The ability of NASs to find automated models suitable for datasets has proved a popular area of experimentation. Deep
-learning also is gaining popularity and is now being widely used. The NAS model has also been designed and expanded
-to enable applications in new tasks such as a NAS for semantic image segmentation, NAS for object detection, and
-NAS for skin lesion classification. The approaches used in NAS are RL Zoph and Le [2016], EA Real et al. [2019], Liu
-et al. [2018a], Lu et al. [2019], and relaxation Liu et al. [2018b]. The three critical steps for NAS are:
-Step 1: Search the CNN model’s search space to find the suitable architecture. A CNN architecture search model
-contains many search spaces which are optimised in the image classification application. Google Brain has launched a
-search space for the NASNet model. It is a feed-forward network in which layers are broken into subgroups called cells.
-The normal cell can learn from images, and the normal cell maintains an image output equal to the input size. However,
-the kernel stride in each reduction cell is 2, halving the input image size. Normal and reduction cells are linked. Each
-cell is stacked during modelling, where Nnormal cells are connected. Reduction cells are added between the Nnormal
-cells, as shown in Fig. 1 (left), to halve the image size, helping the next normal cell to process faster.
-3EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-The output from the search space thereby includes normal cells and reduction cells used in the evaluation. NAS has
-directed acyclic graphs (DAGs) connection between input X1andX2of cells, as shown in Fig. 1 (right). In each
-normal cell, there are two input activations and one output activation. In the first normal cell, input X1andX2are
-copied from the input (image). The next normal cell uses, as input, both the X1from the last normal cell, and the X2
-from the second to last normal cell. All cells are connected the same way until the end of the model. Also, each cell has
-a greater number of cell output channels in each layer.
-Step 2: Evaluate the CNN model on a standard dataset for benchmarks. These benchmarks include number of errors,
-number of parameters, and search cost. The normal cells and reduction cells that are found in the search space are
-evaluated to measure the error rate, the number of parameters, and the computing costs, using CIFAR-10. Due to
-limited processor resources and GPU memory, parameters such as cell count ( N), number of epochs, initial channel,
-and channel increment, are different for each search space and evaluation.
-Step 3: Evaluate with a large-scale dataset. When the model from the search space has been identified, it is evaluated
-with a larger dataset. Model evaluation with the CIFAR-10 dataset cannot be compared with other models because the
-CIFAR-10 dataset contains only ten classes. Given this constraint, CIFAR-100 datasets with 100 classes are required.
-The search space of NASNet used RL and was tested with the CIFAR-10 dataset, which takes up to 2,000 GPU days to
-model. The AmoebaNet Real et al. [2019], based on an evolutionary algorithm model, takes up to 3,150 GPU days for
-the same dataset. Also, the search space of NASNet was designed to use shorter search times. However, the sequential
-model-based optimisation (SMBO) method Liu et al. [2018c] takes 335 GPU days, the gradient descent method Liu
-et al. [2018b] takes just 4 GPU days, whereas weight-sharing across different structures Pham et al. [2018] takes only
-0.5 GPU days.
-As indicated, the AmoebaNet takes 3,150 GPU days, whereas the NSGA-Net Lu et al. [2019], which uses a multi-
-objective evolutionary algorithm to find models, takes 4 GPU days. However, although the error rate of NSGA-Net is
-higher than that of AmoebaNet, based on a standard CIFAR-10 evaluation, the main focus of this area of research has
-been the reduction of search costs.
-2.2 Multi-objective Network Architecture Search
-NAS aims to minimise errors, hyper-parameters, FLOPS, and delays, making it challenging to identify a network
-architecture suitable for each objective simultaneously. Thus, the best network architecture should reduce or minimise
-all of these dimensions. For the evolution-based NASs, NSGA-Nets Lu et al. [2019] considers FLOPS and error count,
-CARS Yang et al. [2020] and LEMONADE Elsken et al. [2018] consider device-agnostic and device-aware objectives.
-In our work, however, we sought the achievement of the three goals; minimising errors, and reducing parameters and
-FLOPS, simultaneously.
-The NASs mostly focus on creating a network architecture for image recognition, then transferring that architecture to
-other tasks. However, for object detection and semantic segmentation, the same network architecture can be used as a
-backbone.
-Many network architectures, such as the EfficientNet Tan and Le [2019], FBNetV2 Wan et al. [2020], DARTS Liu et al.
-[2018b], P-DARTS Chen et al. [2019], CDARTS Yu and Peng [2020], CARS Yang et al. [2020], LEMONADE Elsken
-et al. [2018], NSGA-Net Lu et al. [2019] and NSGA-NetV2 Lu et al. [2020a] were tested only on image recognition
-datasets. It is challenging to design and evaluate a network architecture for general purposes.
-Table 1 shows the research objectives of the various NASs, illustrating that the image identification architectures were,
-in some cases, transferred to object detection, with one, MobileNetV3 Howard et al. [2019] also being applied to
-transfer specifically researched image identification architecture to both object detection and semantic segmentation.
-Our objective was to extend this image identification architecture, using the ImageNet dataset, to object detection,
-semantic segmentation, as well the further purpose of keypoint detection.
-2.3 Inverted Residuals Network (IRN)
-The Inverted Residuals Network (IRN) Tan et al. [2019] concept is needed to reduce the Residuals Network (RN)
-parameters. In contrast, the RN concept integrates data from the previous layer into the last layer. Fig. 2 (left) shows
-that the RN structure has three layers: wide, narrow, and wide approach layers. The wide layers have N16output
-channels whereas the narrow layers have N16channels each. The wide approach layer has N32output channels
-(Nis the input channels in each case). However, all the convolution layers used the standard convolution. Batch
-normalisation (BN) and activation functions (ReLU) were also added into each convolution layer.
-4EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Methods Search Method Multiple Objective Dataset Searched Architecture transfer y
-MobileNetV3 Howard et al. [2019] RL + expert - ImageNet IR, OD, SS
-EfficientNet Tan and Le [2019] RL + scaling - ImageNet IR
-FBNetV2 Wan et al. [2020] gradient - ImageNet IR
-DARTS Liu et al. [2018b] gradient - CIFAR-10 IR
-P-DARTS Chen et al. [2019] gradient - CIFAR-10, CIFAR-100 IR
-PC-DARTS Xu et al. [2020] gradient - CIFAR-10, ImageNet IR, OD
-CDARTS Yu and Peng [2020] gradient - CIFAR-10, ImageNet IR
-CARS Yang et al. [2020] EA Yes CIFAR-10 IR
-LEMONADE Elsken et al. [2018] EA Yes CIFAR-10, CIFAR-100, ImageNet64 IR
-NSGA-Net Lu et al. [2019] EA Yes CIFAR-10 IR
-NSGA-NetV2 Lu et al. [2020a] EA Yes ImageNet IR
-EEEA-Net (this paper) EA+ EE-PI Yes CIFAR-10, ImageNet IR, OD, SS, KD
-yIR = Image Recognition, OD = Object Detection, SS = Semantic Segmentation, KD = Keypoint Detection.
-Table 1: Comparison of different NAS search method with multi-objectives.
-Inverted Residuals Network 
-Input narrow 
-Add Conv 1x1, f=N 
-BN narrow 
-approach wide 
-Depthwise 3x3 
-BN 
-ReLU Conv 1x1, f=Nx16 
-BN 
-ReLU channels = N 
-channels = Nx16 
-channels = Nx16 
-channels = N Xi
-Xi+1 Residual Network 
-Input wide 
-Add Conv 1x1, f=Nx32 
-BN 
-ReLU wide  
-approach narrow 
-Conv 3x3, f=Nx16 
-BN 
-ReLU Conv 1x1, f=Nx16 
-BN 
-ReLU channels = N 
-channels = Nx16 
-channels = Nx16 
-channels = Nx32 Xi
-Xi+1 
-Figure 2: The difference between the residual network (left) and the inverted residual network (right).
-The RN structure is modified and reversed to obtain the IRN. The layers in IRN are defined as narrow layer, wide layer
-and narrow approach layer. In IRN, the number of output channels obtained is equal to the number of input channels,
-N, as shown in Fig. 2 (right). When the data is fed into the 11convolution layer, the number of channels will be
-expanded to N16. The wide layer changes to a 33depth-wise separable convolution instead of a 33standard
-convolution, reducing the FLOPS and number of parameters. There are N16channels in a wide layer, which is equal
-to the previous layer. Also, 11standard convolution is used in the narrow approach to reduce the channels’ size to be
-equal to the input channel N. Then, the input data ( Xi) is combined with the IRN’s output to get data ( Xi+ 1).
-Convolutional layers can be defined in various formats to find a model structure with EA. Convolutional layers are also
-called cells. The types of convolutional layers used in our experiment are presented in Table 2.
-3 Method
-The most commonly used methods to develop NAS are RL and gradient descent algorithms. However, these algorithms
-possess limitations in solving multi-objective problems. EA automates the model search process, is easier to implement,
-and enables discovery of solutions while considering multiple objectives.
-A general description of an EA, including encoding, a presentation of the multi-objective genetic algorithm, and genetic
-operations used with NAS, is provided in Section 3.1. The Early Exit Population Initialisation concept and method, its
-simple, yet effective application in EA, mitigating the complexity and parameters used in the previous models, while
-maintaining the same accuracy, are described in Section 3.2.
-3.1 Evolutionary Neural Architecture Search
-The Genetic Algorithm (GA) is an algorithm based on Darwinian concepts of evolution. The GA is part of a random-
-based EA Xie and Yuille [2017], Baldominos et al. [2017], Real et al. [2017]. GA’s search-based solutions stem from
-the genetic selection of robust members that can survive. The population is integral to GA because GA solutions are
-5EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-kernel Type Layer
-3×3 max and average pooling
-3×3 and 5×5 depth-wise separable convolution
-3×3 and 5×5 dilated convolution
-3×3 and 5×5 inverted residuals convolution
-- skip connection -
-Table 2: Search space of EA-Net and EEEA-Net.
-inv
-3×3 
-inv
-5×5+avg
-3×3 
-max 
-3×3Input 1 
-( X1)
-Input 2 
-( X2)dep 
-3×3 
-skip 
-Concat 
-max 
-3×3 
-dep 
-5×5+
-++
-Output 0
-12
-3L = Type of conv layers [0,1,2,3,4,5,6,7,8] 
-A = Index of cells [ 0]
-B = Index of cells [ 0,1]
-C = Index of cells [ 0,1,2]
-D = index of cells [ 0,1,2,3]Input-1 
-Input-2 Output 
-102
-383
-01
-76
-20
-Chromosome  = LA1LA2,LB1LB2,LC1LC2,LD1LD2 
-             
-   = 3080-0121-1202-6373 
-Figure 3: The chromosome structure of the Evolutionary Algorithm.
-like organisms that evolve after the environment. The most suitable solutions need to rely on genetic diversity. Thus, a
-greater number of genetically diverse populations enables more effective GA solutions.
-The initial phase of GA creates the first population for candidate solutions. The population is determined by population
-size, which describes total solutions. Each solution is called an individual, where an individual consists of a chromosome.
-The chromosome is a mix of genes. In the initial population, it is possible to provide unique information about each
-gene to all other genes, at random. A fitness function computes the fitness value for each individual. CNN’s model
-structure is searched with the NAS search space, defining error rate as a fitness function where fitness value represents
-dataset error value. As shown in Equation 1, the fitness value is calculated where n is the number of individuals.
-fitness (i) =f(xi);i= 1;2;3;::;n (1)
-Organisms consist of different phenotypes and genotypes. Appearances such as foreign features (such as eye colour)
-and internal features (such as blood types) are called phenotypes. Genes of different organisms, called genotypes, can
-be transferred from model to model by gene transfer.
-The CNN model’s architecture is represented as a genotype in the NAS search space. A subset of the NAS search space
-includes normal cells and reduction cells. The cells are stacked in the complete architecture. Normal cells or reduction
-cells consist of connected layers such as convolution layers, average pooling, max pooling, and skip connection. A
-complete model must connect cells to create a genotype for training or testing.
-3.1.1 Encoding
-The genotype of the NAS model consists of normal cells and reduction cells, called chromosomes. There are various
-genes linked to chromosomes. The number of genes is defined as LA 1LA 2,LB 1LB 2,LC 1LC 2, andLD 1LD 2as
-in Equation 2. The gene consists of operations ( L) and indices of operations ( A;B;C;D ). Operation ( L) can be
-considered a type of CNN layer, such as max pooling, average pooling, depth-wise separable convolution, dilated
-convolution, inverted residuals block, and skip connection.
-chromosome (x) =LA 1LA 2;LB 1LB 2;LC 1LC 2;LD 1LD 2 (2)
-For example, consider nine different operations ( L) in the experiment, as [0;1;2;3;4;5;6;7;8]. Moreover, the operation
-index (A;B;C;D ) refers to the operation location to be connected with other operations ( L). From Fig.3 (left), the
-6EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-index is defined as follows: A = [0], B = [0;1], C = [0;1;2], and D = [0;1;2;3]. The connection between operations (L)
-and the operation index ( A;B;C;D ) determines the location of the connection between operations. For example, LA’s
-gene code,LA 1LA 2is 30,80, meaning output data processed in operation 3 and 8 will be linked to index 0.
-Similarly,LB 1LB 2equals 01,21, meaning data processed by operation 0 and 1 are connected at index 1. However, in
-the genes of LC 1LC 2andLD 1LD 2, those genes are linked sequentially to the output of LA 1LA 2andLB 1LB 2, to
-help reduce the number of model parameters. If the LC 1LC 2andLD 1LD 2is connected to the same input as LA 1LA 2,
-andLB 1LB 2(parallel network) will increase the processing time and the parameters.
-PreviousIndex =index2 (3)
-The position of the previous index can be computed from Equation 3, where the index is greater than 1. If the index
-is an even number, it is linked to the even previous index; otherwise, it is linked to the odd previous index. Thus, the
-LC 1LC 2gene is 12-02, which has an index of 2, and the LC 1LC 2gene is linked from index 0. While the LD 1LD 2
-gene is 63-73, it has an index of 3. The LD 1LD 2gene is linked from index 1. However, if there are different indices in
-a gene, for example, a gene 63-72, operator 6 is connected from index 1 and operator 7 is from index 0.
-3.1.2 Multi-objective Genetic Algorithm
-Initially, GA was used for single-objective optimisation problems (SOOP) and, later, GA was developed to solve
-the multi-objective optimisation problem (MOOP) Deb et al. [2002], which has more than one objective function to
-minimise fitness values. The GA that can solve the MOOP problems Carrau et al. [2017], Hasan et al. [2019] is called a
-multi-objective genetic algorithm (MOGA).
-minff1(x);f2(x);:::;f k(x)g
-s.t.x2X(4)
-The optimisation of the CNN model is generally a problem with more than one objective. As illustrated in Equation 4,
-wherefis fitness values, the integer k2is the number of objectives, xis individual, and Xis the set of individuals.
-All these objectives must be as small as possible.
-Indicators used to measure CNNs model performance include model accuracy, model size, and processing speed. There
-are three objectives to consider during a model search: lowest validation error, minimum parameters, and computational
-cost.
-minfError (x);FLOPS (x);Params (x)g
-s.t.x2X
-werror +wflops +wparams = 1
-werror;wflops;wparams>= 0(5)
-The evolutionary algorithm finds the most effective model for each objective by finding the lowest objective values of
-the entire population. We defined the three objective values as being equally important. Thus, it is necessary to set the
-weight of each of the three objective values to 1/3 to find the best model for each value. As illustrated in Equation 5,
-wherexis individual, Xis the individual’s set and werror;wflops;wparams weighs each objective’s weight.
-For the MOOP problem, it is almost impossible to find one solution that provides the optimal value for each objective
-function. For each solution given by the MOOP, the best solution group is called nondominated or Pareto optimal
-because these solutions are compared using the Pareto Domination principle. Many solutions can be obtained from the
-search using MOOP. These solutions will be reviewed again to find the best solution within the searched solution group.
-The best solution group should not dominate when compared to other solutions. For example, any solution vover-
-whelming a better solution can be represented as vw. If no solution vis worse than solution w, then solutions v is
-better thanw.
-3.1.3 Genetic Operations
-The processes used to create offspring in the new generation are called genetic operations. A new population must
-replace an ancestor group that cannot survive. The population can be created in two ways, by crossover or mutation.
-7EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Parent 2 Child =
-: Common 
-: Normal 
-: Mutant 
-(Parent 1) : 40-30- 61-31-00-60-42-13
-(Parent 2) : 40-30- 21-61-22-72-53-11
-(Child) : 40-30- 61-61-02-72-52-13 
-(Original) : 40-30-61-61-02- 72-52-13 
-(Mutant) : 40-30-61-61-02- 33-52-13 
-Crossover : Parent 1 ⊗: Common 
-: Parent 1 
-: Parent 2 
-  Mutation : Normal   Mutant 
-Figure 4: Crossover operation (top): the parent has two different network architectures; the chromosome of each parent
-architecture can be visualised as a digits string. Child architecture was mixed with a chromosome index from their
-parent’s chromosome. Mutation operation (bottom): The location of a normal chromosome was randomly selected.
-Then this pair of genes will be replaced by a new random pair.
-Crossover is the creation of a new population by switching genes of different chromosomes from two populations. The
-genotype of the parent chromosomes will be recombined to create a novel chromosome, which can be done in various
-ways. For example, a point crossover that performs random cutting points or chromosomes to produce offspring is a
-crossover between two chromosomes with a random probability of 0.5. Crossover creates offspring using random genes
-from parents.
-Fig. 4 (top) demonstrates the uniform crossover operation used in this implementation, requiring two-parent architectures.
-We visualised the architectures as follows: 40-30-61-31-00-60-42-13, as parent 1 and 40-30-21-61-22-72-53-11, as
-parent 2. Then, in the crossover operation, the random probability of 0.5 is defined. The fifty-fifty chance was used
-to cross the gene between the two parent architectures for child modelling (40-30-61-61-02-72-52-13). The common
-parent gene is coloured black, but if the gene derived from the first parent is red, then the gene derived from the second
-parent is represented by blue.
-A mutation is an operation to reduce population uniformity and contribute to genetic diversity. The mutation changes
-data in the gene by randomly locating the gene and replacing the original gene with random new genes. The mutation
-causes offspring chromosomes to be different from parents. The individual being mutated is called a mutant.
-Fig. 4 (bottom) shows the mutation operation used during implementation; the location of a chromosome of architecture
-was determined by randomly selecting only one pair of gene locations (72, orange). Then it was replaced with a random
-pair of gene value (33, magenta) of the newly mutated gene.
-Begin Generate the 
-initial populations Encoding Evaluate 
-populations Stop ? Multi-Objective 
-Selection Crossover 
-No
-YesMutation 
-Output 
-FLOPsParamsError 
-MinimizeMinimize
-Minimize
-Non-dominated 
-Population Define maximum 
-of parameters ( 𝞫)searching 
-model? Param 
-< 𝞫 ?No
- return the model 
-with the less 
-parameter Yes
-Early Exit 
-Figure 5: An Early Exit Evolutionary Algorithm.
-8EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Algorithm 1 Multi-objective evolutionary algorithm with an Early Exit Population Initialisation.
-Input: The number of generations G, population size n, validation dataset D, objectivesw.
-Output: A set ofKindividuals on the Pareto front.
-Initialisation: An Early Exit Population Initialisation P1andQ1.
-fori= 1toGdo
-Ri=Pi[Qi
-for allp2Rido
-Train model ponD
-Evaluate model ponD
-end for
-M=tournament-selection (Pi+1)
-F=non-dominated-sorting (Ri)
-Picknindividuals to form Pi+1by ranks and the crowding distance weighted bywbased on Equation 5
-Qi+1=crossover (M)[mutation (M)
-end for
-SelectKmodels at an equal distance near Pareto front from PG
-3.2 Early Exit Population Initialisation (EE-PI)
-EA can find cell patterns by selecting the best model with the lowest error rate. However, the discovery process takes
-longer to search and select cells in each generation. Each population has to be trained and evaluated, which increases
-the time needed to find cell patterns. The single-objective EA uses the error value to find network architecture. However,
-the network architecture with the lowest error may have too many parameters. In our experiment, the maximum number
-of generations was set to 30 with 40 populations per generation due to the limited resources of a single GPU.
-The population is the only factor affecting processing time. The evaluation must examine the entire population to select
-the population with the lowest error rate, thus obtaining an effective model structure. However, a longer search time is
-required to evaluate every population using a single processor unit. Therefore, the EE-PI method was introduced into
-the evolutionary algorithm to reduce the search time and control the number of parameters, as illustrated in Fig. 5 and
-detailed in Algorithm 1.
-The EE-PI method filters the CNN models based on the number of parameters in the network architecture, which is
-iteratively compared to a pre-set maximum value ( ). The EE-PI obtains the CNN network architecture which has less
-parameters than the maximum number of parameters attached to the EA, as illustrated in Fig. 5, which shows the Early
-Exit as the dashed-line block.
-EarlyExit (;),
-1;if
-0;otherwise(6)
-In Equation 6, where is the parameter of the model which is discovered, is the maximum number of parameters. If
-the number of parameters found in the model are less than or equal to the maximum number of parameters ( ),
-then the model will be considered as a part of the first-generation population.
-For example, to select a network architecture with a maximum of 3 million parameters ( = 3), EA selects the model
-by considering the number of parameters lower than the maximum number of parameters. Suppose network architecture
-is not considered, because it has more than maximum parameters ( ). In this case, it chooses a new structure with less
-than 3 million parameters. Therefore, in conjunction with the EA in the selection process, Early Exit facilitates the
-filtering out of the model with the number of parameters greater than the maximum number of parameters. The best
-network architecture is also discovered using the EA with Early Exit by considering the error rate and the number of
-parameters.
-4 Experiments and Results
-Experiments were carried out in three parts: First, finding and evaluating the network architecture with EEEA on
-CIFAR-10 and CIFAR-100. The second part was the finding and evaluation of the EEEA-Net using the ImageNet
-datasets. In the third part, the EEEA-Net obtained from the second part is applied for other tasks such as object detection,
-semantic segmentation, and keypoint detection. The PyTorch deep learning library was used in the experiments. The
-9EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-experiment was carried out on Intel(R) Xeon(R) W-3235 CPU @ 3.30GHz 12 Core CPU, 192 GB RAM and NVIDIA
-RTX 2080 Ti GPU, running on the Ubuntu 18.04.3 operating systems.
-4.1 CIFAR-10 and CIFAR-100 datasets
-This subsection searched for a model with the CIFAR-10 dataset; it was evaluated on CIFAR-10 and CIFAR-100
-datasets. Both CIFAR-10 and CIFAR-100 datasets consisted of 60,000 images, with 50,000 images and 10,000 images
-in the training set and test set, respectively. The CIFAR-10 and CIFAR-100 have 10 and 100 classes, respectively, with
-600 images in each class.
-4.1.1 Architecture Search on CIFAR-10
-Thirty generations with 40 populations in each generation were defined to locate the network architecture with EA. The
-first-generation populations were randomly generated, with subsequent populations in generations 2-30 being evolved
-with EA. Each population was defined with a depth of two normal cells instead of the usual six normal cells. Thus, it
-reduced the search time of the network architecture. The search and evolution process happens more rapidly when
-Early Exit is used in the initial populations’ process. Early Exit selects the network architecture having less than the
-pre-specified maximum of parameters ( ). Thus, population evolution will choose only network architectures that are
-efficient and have fewer parameters.
-The hyper-parameters for the search process were defined as: the total number of cells (normal cells and reduce cells)
-equal to eight layers with 32 initial channels by training the network from scratch for one epoch on the CIFAR-10
-dataset. The hyper-parameters used included a batch size of 128, with SGD optimiser with weight decay equal to
-0.0003 and momentum equal to 0.9. The initial learning rate was 0.05. Using the cosine rule scheduler, the Cutout
-regularisation had a length set to 16, a drop-path of the probability of 0.2, and the maximum number of parameters
-equal to 3, 4, and 5 million.
-The evolutionary algorithm (EA-Net, = 0) took 0.57 GPU days to find the network architecture with NVIDIA RTX
-2080 Ti. However, the early exit evolutionary algorithm (EEEA-Net-A, = 3) took 0.38 GPU days, EEEA-Net-B
-(= 4) took up to 0.36 GPU days, and EEEA-Net-C ( = 5) took up to 0.52 GPU days. These architectures are used
-for performance evaluation in the next section.
-4.1.2 Architecture Evaluation on the CIFAR-10 dataset
-The network architecture had to be changed to find the normal and reduced cells with the Early Exit evolutionary
-algorithms. The CIFAR-10 dataset was used for the evaluation. The hyper-parameters were defined with the number of
-all cells (normal and reduce cells) set to 20 layers with 32 initial channels, the network was trained from scratch with
-600 epochs with a batch size of 96, SGD optimiser with weight decay was 0.0003 and momentum 0.9, and the initial
-learning rate set to 0.025. Using the cosine rule scheduler, the Cutout regularisation had a length set to 16, a drop-path
-of the probability of 0.2, and auxiliary towers of weight equal to 0.4.
-Table 3 shows the comparisons and evaluations of EEEA-Net with other state-of-the-art models. EEEA-Net-C was
-evaluated with the test dataset, giving an error rate of 2.46% for CIFAR-10. It took 0.52 GPU days to find normal and
-reduce cells.
-By comparison, our EEEA-Net-C model achieved a lower error rate and search time than all of those other models.
-AmoebaNet-B was the lowest of the other state-of-the-art models: NASNet-A model, PNAS, both DARTS versions,
-and NSGA-Net. However, the AmoebaNet-B model required 3,150 GPU days to complete, according to Real et al.
-[2019]. This is clearly a hugely greater amount of search resources than required in our model (0.52 GPU days).
-4.1.3 Performance and Computational Complexity Analysis
-The multi-objective search tested error rate, number of FLOPS, and parameters. Optimisation on the effectiveness
-of a multi-objective uses Hypervolume (HV) as a measure of performance that computes the dominated area, using
-a reference point (Nadir point) with the most significant objective value from the first-generation population. Then,
-the Pareto-frontier solution computes the area between the reference point and Pareto. The higher HV shows that a
-multi-objective solution performs better in all objectives.
-After the model search, the HV for all the solutions obtained from the search is calculated to compare the performance
-of two variants: a model that uses Early Exit (EA-Net) and a model that does not use Early Exit. In Fig. 6, the values
-shown in the vertical axis are normalised HV , and the horizontal axis is generations. When we closely look at the HV
-value, it was found that the search using the EA-Net model yielded HV values greater than the three EEEA-Net models.
-10EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-ArchitectureCIFAR-10 Error
-(%)CIFAR-100 Error
-(%)Params
-(M)Search cost
-(GPU days)Search
-Method
-NASNet-A + CO Zoph and Le [2016] 2.83 16.58 3.1 2,000 RL
-ENAS + CO Pham et al. [2018] 2.89 17.27 4.6 0.5 RL
-PNAS Liu et al. [2018c] 3.41 17.63 3.2 225 SMBO
-DARTS-V1 + CO Liu et al. [2018b] 2.94 - 2.9 1.5 gradient
-DARTS-V2 + CO Liu et al. [2018b] 2.83 17.54 3.4 4 gradient
-P-DARTS + CO Chen et al. [2019] 2.50 15.92 3.4 0.3 gradient
-PC-DARTS + CO Xu et al. [2020] 2.57 17.36 3.6 0.1 gradient
-CDARTS + CO Yu and Peng [2020] 2.48 15.69 3.8 0.3 gradient
-AmoebaNet-A + CO Real et al. [2019] 3.12 18.93 3.1 3,150 evolution
-AmoebaNet-B + CO Real et al. [2019] 2.55 - 2.8 3,150 evolution
-LEMONADE Elsken et al. [2018] 3.05 - 4.7 80 evolution
-NSGA-Net + CO Lu et al. [2019] 2.75 20.74 3.3 4 evolution
-CARS-I + CO Yang et al. [2020] 2.62 16.00 3.6 0.4 evolution
-EA-Net (= 0) + CO 3.30 17.58 2.9 0.57 evolution
-EEEA-Net-A ( = 3) + CO 3.69 20.16 1.8 0.34 evolution
-EEEA-Net-B ( = 4)+ CO 2.88 16.90 1.8 0.36 evolution
-EEEA-Net-C ( = 5)+ CO 2.46 15.02 3.6 0.52 evolution
-Table 3: Comparing EEEA-Net with other architectures from RL, SMBO, gradient, and evolution search method on
-CIFAR-10 and CIFAR-100 datasets.
-0 5 10 15 20 25 30
-Generation0.000.020.040.060.080.100.120.14Normalized Hypervolume
-EA-Net
-EEEA-Net-A
-EEEA-Net-B
-EEEA-Net-C
-Figure 6: Performance Metric of EA-Net and EEEA-Nets.
-However, considering only the model with an early exit, it was found that searches using equal to 5 performed
-better thanequal to 3 and 4, since is a parameter that determines the model size by the number of parameters.
-Consequently, creating larger models by increasing the size of , gave superior performance.
-In addition, when considering a model without an Early Exit (EA-Net) and a model that used an Early Exit ( =5,
-EEEA-Net-C), it was found that the search efficiency of the EEEA-Net-C model was nearly similar to that of EA-Net
-because the EA-Net search does not control the model size while searching for the model. Therefore, the model obtained
-by EA-Net’s search may is likely to obtain a model of a large parameter size. On the other hand, the model with an
-Early Exit better controls the model size, and the resulting model provides similar performance than achievable in an
-uncontrolled search.
-11EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-0 5 10 15 20 25 30
-Generations404550556065CIFAR-10 Accuracy (%)
-Search space of EA-Net
-Pareto-front
-(a) CIFAR-10 Accuracy vs Generations.
-0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
-FLOPs (M)45.047.550.052.555.057.560.062.565.0CIFAR-10 Accuracy (%)
-Search space of EA-Net
-Pareto-front (b) CIFAR-10 Accuracy vs FLOPS.
-0 5 10 15 20 25 30
-Generations404550556065CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-A
-Pareto-front
-(c) CIFAR-10 Accuracy vs Generations.
-0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
-FLOPs (M)45.047.550.052.555.057.560.062.565.0CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-A
-Pareto-front (d) CIFAR-10 Accuracy vs FLOPS.
-0 5 10 15 20 25 30
-Generations404550556065CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-B
-Pareto-front
-(e) CIFAR-10 Accuracy vs Generations.
-0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
-FLOPs (M)45.047.550.052.555.057.560.062.565.0CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-B
-Pareto-front (f) CIFAR-10 Accuracy vs FLOPS.
-0 5 10 15 20 25 30
-Generations404550556065CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-C
-Pareto-front
-(g) CIFAR-10 Accuracy vs Generations.
-0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
-FLOPs (M)45.047.550.052.555.057.560.062.565.0CIFAR-10 Accuracy (%)
-Search space of EEEA-Net-C
-Pareto-front (h) CIFAR-10 Accuracy vs FLOPS.
-Figure 7: Progress of trade-offs after each generation of EA-Net and EEEA-Nets.
-12EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-We present progress trade-offs after each generation of the EA-Net and EEEA-Nets search through Fig. 7. The whole
-population is demonstrated by two-dimensional coordinates such as CIFAR-10 accuracy vs. Generations and CIFAR-10
-accuracy vs. FLOPS.
-4.1.4 Data augmentation
-The results from the evaluation of EEEA-Net are represented in precision floating-point (FP32). Our experimental goals
-were to create the most effective EEEA-Net without modifying the model structure. In the evaluation, we added the
-AutoAugment (AA) Cubuk et al. [2019] technique to the data augmentation process. AA created a more diverse set of
-data, making the model more effective. When Cutout DeVries and Taylor [2017] and AA Cubuk et al. [2019] were
-used, we observed that the error rate of EEEA-Net-C was reduced to 2.42%. Without AA, however, an error rate of
-2.46% occurred, as shown in Table 4.
-4.1.5 Architecture Evaluation on CIFAR-100 dataset
-The AA technique was used to optimise the search and evaluation process of the EEEA-Net. The EEEA-Net-C
-was trained using the CIFAR-10 dataset, which was not sufficient for our purposes. Consequently, the EEEA-Net
-architectures obtained from CIFAR-10 dataset were used with CIFAR-100.
-The hyper-parameters used in the training process were changed to evaluate the EEEA-Net with the CIFAR-100 dataset,
-where the number of all cells (normal and reduce cells) was set to 20 layers with 36 initial channels. This was the
-outcome of training the network from scratch in 600 epochs with a batch size of 128, setting the SGD optimiser with a
-weight decay of 0.0003 and momentum of 0.9, and the initial learning rate set to 0.025 and running with the cosine rule
-scheduler. The Cutout regularisation length was equal to 16, and the drop-path of probability was 0.2, with auxiliary
-towers of the weight of 0.4.
-When the EEEA-Net-C (same model structure) was evaluated with CIFAR-100 datasets, it showed an error rate of
-15.02%, as shown in Table 3. Further, this evaluation, with 3.6 million parameters, resulted in the lowest error rate of all
-the state-of-the-art models.
-4.2 ImageNet dataset
-In this subsection, we used the ImageNet dataset for the search and model evaluation. The ImageNet dataset is a
-large-scale standard dataset for benchmarking performance for image recognition for 1,000 classes with 1,281,167
-images for the training set, 50,000 images for the test set, divided into 1,000 classes.
-4.2.1 Architecture Search on ImageNet
-Early Exit was used to discover a network architecture using the CIFAR-10 dataset. However, this network architecture
-was constructed from a multi-path NAS, which requires considerable memory. Given this, we used a single-path NAS
-to find the network architecture on ImageNet to reduce this search time, which also allows a multi-objective search
-with early exit population initialisation to be used on the OnceForAll Cai et al. [2020] super-network (called Supernet)
-to discover all network architectures that offer the best trade-off. Supernet can also search for the four dimensions
-of the network architecture, including kernel size, width (number of channels), depth (number of layers), and input
-resolution resize. We set all hyper-parameters for our architecture searches following the process in NSGA-NetV2 Lu
-et al. [2020a].
-The two objectives of accuracy and FLOPS were the criteria for searching for 300 high accuracy samples with low
-FLOPS. However, these sample architectures have a diverse number of parameters. The number of parameters affects
-ArchitectureCIFAR-10 Error
-(%)Training time
-(GPU Hours)AutoAugment
-EEEA-Net-A ( = 3) + CO 3.69 25.75 -
-EEEA-Net-B ( = 4) + CO 2.88 25.95 -
-EEEA-Net-C ( = 5) + CO 2.46 48.05 -
-EEEA-Net-A ( = 3) + CO 3.35 30.38 Yes
-EEEA-Net-B ( = 4) + CO 2.87 31.26 Yes
-EEEA-Net-C ( = 5) + CO 2.42 54.26 Yes
-Table 4: Results of CIFAR-10 using Cutout (CO) and AutoAugment (AA).
-13EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-architecture size when running on devices that may have memory constraints. Thus, to prevent the architecture from
-having too many parameters, we appended the Early Exit to create the first population with limited parameters.
-In this experiment, we compiled the number of architecture parameters shown in Table 5 to calculate the average
-number of parameters equal to 5. Thus, the maximum number of parameters ( ) whereequals 5, 6 or 7, was defined
-as follows: EEEA-Net-A ( = 5), EEEA-Net-B ( = 6), EEEA-Net-C ( = 7). For a fair comparison, we set qual
-to 0, and called that EA-Net-N ( = 0). We categorised our networks using the number of MobilenetV3 FLOPS to
-define the network size architectures of EEEA-Net-A1, EEEA-Net-B1, EEEA-Net-C1, and EEEA-Net-N1 as small-
-scale architectures ( <155 FLOPS). The large-scale architectures ( <219 FLOPS) are EEEA-Net-A2, EEEA-Net-B2
-EEEA-Net-C2, and EEEA-Net-N2.
-4.2.2 Architecture Evaluation on ImageNet dataset
-The discovery of architecture from Supernet is the separation of some layers from Supernet called subnets. Since
-Supernet and the subnets have different network architectures, the accuracy of the subnets, with pre-trained weight from
-Supernet, is very low when they were tested on the validation dataset. So, the subnets have calibrated the statistics of
-batch normalisation (BN) after searching on Supernet. The new BN statistics from the subnets were calculated using
-the validation dataset and updating the BN of all of the subnets. Thus, BN calibration can improve test accuracy value
-efficiency with the ImageNet dataset.
-Table 5 shows the comparison of EEEA-Net performance with other models, using three main comparison factors: error
-rate, number of parameters, and FLOPS. We classify models using architectural search methods such as auto, manual,
-or a combination. When comparing our small-scale architectures (EEEA-Net-A1, EEEA-Net-B1, EEEA-Net-C1, and
-EEEA-Net-N1) with GhostNet 1.0 Han et al. [2020], we found that all our architectures outperform GhostNet 1.0.
-Also, EEEA-Net-A1, EEEA-Net-B1, EEEA-Net-C1, and EEEA-Net-N1 provide lower error and FLOPS counts than
-MobileNetsV3 Large 0.75 Howard et al. [2019]. However, the MobileNetsV3 Large 0.75 has fewer parameters than our
-models.
-Similarly, when we compared our large-scale architectures with other architectures, we found that EEEA-Net-C2
-(= 7) has a Top-1 error, and FLOPS were lower than all other architectures, as shown in Table 4. When we compare
-our architecture with MobileNetsV3 Large 1.0, EEEA-Net-C2 provides a 1% less error value than MobileNetsV3 [28],
-and the FLOPS count of EEEA-Net-C2 is reduced by 2 Million from MobileNetsV3. However, EEEA-Net-C2 had 0.6
-Million more parameters than MobileNetsV3.
-Model Top-1 Error (%) Top-5 Error (%) Params (M) FLOPS (M) Type
-GhostNet 1.0 Han et al. [2020] 26.1 8.6 5.2 141 manual
-MobileNetsV3 Large 0.75 Howard et al. [2019] 26.7 - 4.0 155 combined
-EA-Net-N1 (ours) 26.1 8.6 4.4 140 auto
-EEEA-Net-A1 ( = 5)(our) 26.3 8.8 5.0 127 auto
-EEEA-Net-B1 ( = 6) (our) 26.0 8.5 5.0 138 auto
-EEEA-Net-C1 ( = 7) (our) 25.7 8.5 5.1 137 auto
-MobileNetsV1 Howard et al. [2017] 29.4 - 4.2 575 manual
-MobileNetsV2 Sandler et al. [2018] 28.0 - 3.4 300 manual
-GhostNet 1.3 Han et al. [2020] 24.3 7.3 7.3 226 manual
-MobileNetsV3 Large 1.0 Howard et al. [2019] 24.8 - 5.4 219 combined
-NASNet-A Zoph and Le [2016] 26.0 8.4 5.3 564 auto
-MnasNet-A1 Tan et al. [2019] 24.8 7.5 3.9 312 auto
-FBNet-C Wu et al. [2019] 25.1 - 5.5 375 auto
-MOGA-A Chu et al. [2020] 24.1 7.2 5.1 304 auto
-FairNAS-A Chu et al. [2019] 24.7 7.6 4.6 388 auto
-PNASNet-5 Liu et al. [2018c] 25.8 8.1 5.1 588 auto
-NSGANetV1-A2 Lu et al. [2020b] 25.5 8.0 4.1 466 auto
-OnceForAll Cai et al. [2020] 24.0 - 6.1 230 auto
-MSuNAS Cai et al. [2020] 24.1 - 6.1 225 auto
-EA-Net-N2 (ours) 24.4 7.6 5.9 226 auto
-EEEA-Net-A2 ( = 5) (our) 24.1 7.4 5.6 198 auto
-EEEA-Net-B2 ( = 6) (our) 24.0 7.5 5.7 219 auto
-EEEA-Net-C2 ( = 7) (our) 23.8 7.3 6.0 217 auto
-Table 5: Comparing EEEA-Net with other architectures from manual, combined, and auto search method on ImageNet
-datasets.
-14EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-140 160 180 200 220
-FLOPs (M)73.574.074.575.075.576.0ImageNet Top-1 Accuracy (%)
-EA-Net
-EEEA-Net (=5)
-EEEA-Net (=6)
-EEEA-Net (=7)
-MobileNetV3
-GhostNet
-(a) Top-1 accuracy vs FLOPS
-4.0 4.5 5.0 5.5 6.0 6.5 7.0
-Params (M)73.574.074.575.075.576.0ImageNet Top-1 Accuracy (%)
-EA-Net
-EEEA-Net (=5)
-EEEA-Net (=6)
-EEEA-Net (=7)
-MobileNetV3
-GhostNet (b) Top-1 accuracy vs Parameters
-Figure 8: Comparison of Top-1 accuracy, FLOPS (left), and parameters (right) between EEEA-Nets and MobileNetV3
-[28] and GhostNet [40] on ImageNet dataset.
-We chose MobileNetV3 and GhostNet, including both small and large versions, to compare with our architecture, as
-shown in Fig. 8. Overall, we observed that EEEA-Net-C ( = 7) significantly outperforms MobileNetV3 and GhostNet
-for Top-1 accuracy and FLOPS. Furthermore, EEEA-Net-C ( = 7) achieves lower parameters than GhostNet.
-4.3 Architecture Transfer
-After searching and evaluating the model using the ImageNet dataset for image recognition, the models trained with the
-ImageNet dataset can be further developed and applied to object detection, semantic segmentation and human keypoint
-detection applications.
-4.3.1 Object detection
-EEEA-Net-C2 ( = 7) was used as the backbone for the object detection task to compare the effectiveness of our
-architecture on a real-world application. We utilised the same architecture trained with ImageNet datasets on the
-firmware called Single-Shot Detectors (SSD) Liu et al. [2016] and You Only Look Once version four (YOLOv4)
-Bochkovskiy et al. [2020].
-PASCAL VOC is a standard set of data used to measure an architecture’s performance with object detection datasets. It
-consists of 20 classes, with bottles and plants being small objects with the lowest Average Precision (AP) of all classes.
-We used the SSDLite framework Sandler et al. [2018] for fast and optimised processing on mobile devices. We also
-used the YOLOv4 framework Bochkovskiy et al. [2020] for high precision object detection.
-All models were trained on the PASCAL VOC 2007 and VOC 2012 Everingham et al. [2010] train set by training the
-network for 200 epochs with a batch size of 32, SGD optimiser with weight decay equal to 0.0005 and momentum
-equal to 0.9, the initial learning rate is 0.01. It uses the scheduler with the cosine rule without a restart. All input images
-are resized to 320320pixels, and these models were used to evaluate the PASCAL VOC test set.
-For YOLOv4, we adopted MobileNet-V2 Sandler et al. [2018], MobileNet-V3 Howard et al. [2019] and EEEA-Net-C2
-as the backbone of YOLOv4. All models were trained with 140 epochs and a batch size of 4. All inputs are random
-scales with multi-scale images ranging from 320 to 640 pixels. The label smoothing is 0.1, SGD optimiser with weight
-decay equal to 0.0005 and momentum equal to 0.9. The initial learning rate was set to 0.01 for a scheduler with a cosine
-rule with the warm-up strategy performed twice.
-Table 6 shows the performance of our architecture for object detection. EEEA-Net-C2 achieved a higher AP than NAS-
-Net, DARTS, ShuffleNet-V2, MobileNet-V2, MobileNet-V3, and MnasNet for the SSDLite framework. Nonetheless,
-EEEA-Net-C2 has 152 more million FLOPS than MobileNet-V3. For fairness, we used MobileNet-V2, MobileNet-V3
-and EEEA-Net-C2 for training and evaluated these models using the PASCAL VOC test dataset via the YOLOv4
-framework. The EEEA-Net-C2 significantly outperformed both MobileNet-V2 and MobileNet-V3.
-15EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Model Framework Params (M) FLOPS (M)Small Objects AP (%)VOC2007 mAP (%)Bottle Plant
-NASNet Zoph and Le [2016] SSDLite 5.22 1238 41.5 46.1 71.6
-DARTS Liu et al. [2018b] SSDLite 4.73 1138 38.3 49.3 71.2
-ShuffleNet-V2 Ma et al. [2018] SSDLite 2.17 355 29.9 38.1 65.4
-MobileNet-V2 Sandler et al. [2018] SSDLite 3.30 680 37.9 43.9 69.4
-MobileNet-V3 Howard et al. [2019] SSDLite 3.82 485 38.1 45.6 69.2
-MnasNet Tan et al. [2019] SSDLite 4.18 708 37.7 44.4 69.6
-EEEA-Net-C2 (ours) SSDLite 5.57 637 40.9 48.9 71.7
-MobileNet-V2 Sandler et al. [2018] YOLOv4 46.34 8740 66.4 58.4 81.5
-MobileNet-V3 Howard et al. [2019] YOLOv4 47.30 8520 68.2 50.7 78.9
-EEEA-Net-C2 (ours) YOLOv4 31.15 5540 68.6 56.7 81.8
-Table 6: Result of Object detection with different backbones on PASCAL VOC 2007 test set.
-Model Params (M) FLOPS (G) mIoU (%)
-NASNet Zoph and Le [2016] 7.46 36.51 77.9
-DARTS Liu et al. [2018b] 6.64 34.77 77.5
-ShuffleNet-V2 Ma et al. [2018] 4.10 26.30 73.0
-MobileNet-V2 Sandler et al. [2018] 5.24 29.21 77.1
-MobileNet-V3 Howard et al. [2019] 5.60 27.09 75.9
-MnasNet Tan et al. [2019] 6.12 29.50 76.8
-EEEA-Net-C2 (ours) 7.34 28.65 76.8
-Table 7: Results of BiSeNet with different backbones on Cityscapes validation set. (single scale and no flipping).
-4.3.2 Semantic Segmentation
-The cityscape dataset Cordts et al. [2016] was chosen to experiment with semantic segmentation. It is a large-scale
-dataset of street scenes in 50 cities. Cityscapes provide dense pixel annotations of 5,000 images. These images were
-divided into three groups of 2,975, 500, 1,525 images for training, validation, and testing. We used BiSeNet Yu et al.
-[2018] with different backbones to evaluate our architecture’s performance for semantic segmentation on the Cityscapes
-dataset. NASNet, DARTS, ShuffleNet-V2, MobileNet-V2, MobileNet-V3, MnasNet, and EEEA-Net-C2 have trained
-80,000 iterations with a poly learning scheduler at an initial learning rate of 0.01 and batch size equals 16. All training
-images were resized to 10241024 pixels using image augmentation using colour jitter, random scale, and random
-horizontal flip.
-Table 7 shows that ShuffleNet-V2 achieved a smaller number of parameters and lower FLOPS than other architectures.
-However, MobileNet-V2 achieved a greater Mean Intersection over Union (mIoU) than ShuffleNet-V2, MobileNet-V3,
-MnasNet, and EEEA-Net-C2. The mIoU of EEEA-Net-C2 is the same as MnasNet. It is better than ShuffleNet-V2 and
-MobileNet-V3.
-4.3.3 Keypoint Detection
-Human keypoint detection, also known as human pose estimation, is the visual sensing of human gestures from
-keypoints such as the head, hips, or ankles. MS COCO Lin et al. [2014] is a comprehensive dataset to measure keypoint
-detection performance, consisting of data for 250,000 persons, with the data labelle at 17 keypoints. SimpleBaseline
-Xiao et al. [2018] is a framework for keypoint detection, enabling easier changes to backbones. Given this, it allowed
-us to adapt to other architectures more simply.
-All architectures were trained on the MS COCO train2017 set by training the network for 140 epochs with a batch size
-of 128, Adam optimiser, the initial learning rate is 0.001, which is reduced to 0.0001 at 90th epoch and reduced to
-0.00001 at 120th epoch. The training set is resized to 256192pixels using random rotation, scale, and flipping data.
-Table 8 shows the experimental result of SimpleBaseline with different backbones. Our EEEA-Net-C2 performed better
-than other backbones in the number of parameters. As well, EEEA-Net-C2 outperforms small architectures (excluding
-NASNet and DARTS).
-16EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Model Params (M) FLOPS (M) AP (%)
-NASNet Zoph and Le [2016] 10.66 569.11 67.9
-DARTS Liu et al. [2018b] 9.20 531.77 66.9
-ShuffleNet-V2 Ma et al. [2018] 7.55 154.37 60.4
-MobileNet-V2 Sandler et al. [2018] 9.57 306.80 64.9
-MobileNet-V3 Howard et al. [2019] 9.01 223.16 65.3
-MnasNet Tan et al. [2019] 10.45 320.17 62.5
-EEEA-Net-C2 (ours) 7.47 297.49 66.7
-Table 8: Results of SimpleBaseline with different backbone settings on MS COCO2017 validation set. Flip is used
-during validation.
-4.4 Limitations
-The development of a NAS search with only one GPU processor was a challenge for the reasons set out below. Setting
-the appropriate number of populations, the number of generations of each population, and the number of search epochs
-suitable for one GPU processor presents considerable difficulties. All of these parameters affect the model search time.
-Increasing the number of generations increases the computing cost, but increasing the number of generations provides an
-opportunity for greater recombination of populations, thereby maximising the efficiency of discovering new populations.
-Moreover, an increased number of search epoch helps improve each population’s error fitness value.
-All these restrictions help to improve the NAS search. However, increasing these numbers influences search time. For
-example, increasing the number of search epochs from 1 epoch to 10 epochs results in a 10 increase in search time.
-5 Conclusion
-We achieved our research goals by successfully developing a CNN architecture suitable for an on-device processor with
-limited computing resources and applying it in real-world applications.
-This outcome was achieved by significantly reducing the computational cost of a neural architecture search. We
-introduced the Early Exit Population Initialisation (EE-PI) for Evolutionary Algorithm method to create the EEEA-Nets
-model. Our method achieved a massive reduction in search time on CIFAR-10 dataset; 0.34 to 0.52 GPU days. This
-must be seen as an outstanding outcome compared against other state-of-the-art models, such as the NSGA-Net model,
-which required 4 GPU days, the 2,000 GPU days of the NASNet model and the 3,150 GPU days of the AmoebaNet
-model.
-In the EEEA-Nets architecture, our emphasis was on reducing the number of parameters, the error rate and the
-computing cost. We were able to achieve this by introducing an Early Exit step into the Evolutionary Algorithm.
-Our EEEA-Nets architectures were searched on image recognition task, transferring architectures to other tasks.
-Experimentally, EEEA-Net-C2 is significantly better than MobileNet-V3 on image recognition, object detection,
-semantic segmentation, and keypoint detection tasks. Addressing this latter task had not been achieved or even
-attempted in any other CNN model. Therefore, our architectures can be deployed on devices with limited memory
-and processing capacity by achieving these significant reductions, allowing real-time processing on smartphones or
-on-device systems.
-The task of optimising the search for multi-objective evolutionary algorithms shall be continued as our future work to
-find better-performing models. In addition, we will consider applying a multi-objective evolutionary algorithm with
-EE-PI to find mobile-suitable models in other applications such as marine detection or pest detection.
-Acknowledgements
-The authors would like to acknowledge the Thailand Research Fund’s financial support through the Royal Golden
-Jubilee PhD. Program (Grant No. PHD/0101/2559). The study was undertaken using the National Computational
-Infrastructure (NCI) in Australia under the National Computational Merit Allocation Scheme (NCMAS). Further, we
-would like to extend our appreciation to Mr Roy I. Morien of the Naresuan University Graduate School for his assistance
-in editing the English grammar, syntax, and expression in the paper.
-17EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
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-6 Appendix
-6.1 Architecture Visualisation
-This section visualises the architectures obtained by searching for EEEA-Nets with CIFAR-10 datasets, as shown in
-Fig. 9, and EEEA-Nets with ImageNet datasets shown in Fig. 10. These architectures are the most reliable, minimising
-three goals.
-20EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-c_{k-2} 0dil_conv_3x3
-max_pool_3x3
-c_{k-1}1inv_res_5x5
-dil_conv_5x5
-2avg_pool_3x3
-avg_pool_3x33inv_res_3x3
-c_{k}inv_res_5x5
-4max_pool_3x3
-sep_conv_3x3
-(a) EA-Net ( = 0) Normal Cell.
-c_{k-2}0avg_pool_3x3
-inv_res_3x3
-1dil_conv_3x32inv_res_3x3
-dil_conv_5x54max_pool_3x3sep_conv_3x3
-c_{k-1}inv_res_5x5c_{k}
-3max_pool_3x3
-skip_connect (b) EA-Net ( = 0) Reduction Cell.
-c_{k-2}0max_pool_3x3
-inv_res_3x3
-1max_pool_3x32inv_res_3x3
-c_{k-1} dil_conv_3x3skip_connect
-3avg_pool_3x3
-c_{k}
-skip_connect
-4skip_connectavg_pool_3x3
-(c) EEEA-Net-A ( =3.0) Normal Cell.
-c_{k-2}0
-skip_connectdil_conv_3x3
-1avg_pool_3x32 dil_conv_3x3avg_pool_3x3
-4dil_conv_5x5
-c_{k-1}max_pool_3x33skip_connect
-c_{k}skip_connect
-dil_conv_3x3 (d) EEEA-Net-A ( =3.0) Reduction Cell.
-c_{k-2} 0skip_connect
-max_pool_3x3
-c_{k-1}
-1max_pool_3x3skip_connect2 skip_connect
-3sep_conv_3x3skip_connect
-c_{k}
-dil_conv_3x34max_pool_3x3
-sep_conv_3x3
-(e) EEEA-Net-B ( =4.0) Normal Cell.
-c_{k-2} 0skip_connectinv_res_5x5
-1avg_pool_3x3
-c_{k-1}dil_conv_3x3
-2
-inv_res_3x3skip_connect3max_pool_3x3
-avg_pool_3x34avg_pool_3x3
-c_{k}dil_conv_3x3 (f) EEEA-Net-B ( =4.0) Reduction Cell.
-c_{k-2}0dil_conv_5x5
-max_pool_3x3
-1inv_res_5x5
-inv_res_3x3c_{k-1}
-3sep_conv_5x54sep_conv_3x32
-dil_conv_5x5inv_res_3x3
-skip_connectc_{k}avg_pool_3x3
-(g) EEEA-Net-C ( =5.0) Normal Cell.
-c_{k-2}
-0inv_res_3x3
-dil_conv_3x31avg_pool_3x3
-inv_res_3x3
-c_{k-1} 2dil_conv_5x5
-max_pool_3x34inv_res_3x3c_{k}3skip_connect
-max_pool_3x3
-inv_res_5x5 (h) EEEA-Net-C ( =5.0) Reduction Cell.
-Figure 9: Normal and Reduction cells learned on CIFAR-10: EA-Net ( =0.0), EEEA-Net-A ( =3.0), EEEA-Net-B
-(=4.0), and EEEA-Net-C ( =5.0).
-21EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-K=3 
-E=3 K=3 
-E=4 K=3 
-E=6 K=5 
-E=3 K=5 
-E=4 K=5 
-E=6 K=7 
-E=3 K=7 
-E=4 K=7 
-E=6 Skip Legend 
-Stem Tail Stem Tail
-Stem TailStage 1 Stage 2 Stage 3 Stage 4 Stage 5 
-EA-Net-N1 EA-Net-N2 
-Stem TailEEEA-Net-A1 
-( 𝛽 = 5) EEEA-Net-A2 
-( 𝛽 = 5) 
-Stem Tail Stem Tail
-Stem TailEEEA-Net-B1 
-( 𝛽 = 6) EEEA-Net-B2 
-( 𝛽 = 6) 
-Stem TailEEEA-Net-C1 
-( 𝛽 = 7) EEEA-Net-C2 
-( 𝛽 = 7) Stage 1 Stage 2 Stage 3 Stage 4 Stage 5 
-Figure 10: EA-Nets and EEEA-Nets architectures were searched from ImageNet datasets. The stem and tail layers in
-all architectures are the same.
-EEEA -Net-C2
-MobileNetv 3
-MobileNetv 2
-Figure 11: An example of object detection results of MobileNetv2, MobileNetv3, and EEEA-Net-C2 models.
-6.2 Error Analysis of EEEA-Net-C2
-Our experiment applied EEEA-Net-C2 for detection, semantic segmentation, and human keypoint detection, where
-we concluded that the EEEA-Net-C2 model was better than the MobileNet-V3 model. For error analysis of the
-EEEA-Net-C2 model, images for each application were processed to check the correct results. In this appendix, error
-analysis of the EEEA-Net-C2 model is divided into three parts: error analysis of object detection, error analysis of
-semantic segmentation errors and error analysis of human keypoint detection.
-6.2.1 Object detection
-Object detection results from MobileNetv2, MobileNetv3 and EEEA-Net-C2 models are shown in Fig. 11. Given the
-error from the images in the first column, the MobileNetv2 model was found to have mistakenly identified “bird" as
-“person", while the MobileNetv3 model was unable to find “bird". However, the EEEA-Net-C2 model detected the
-location of all “birds".
-As with the second column in Fig. 11, the EEEA-Net-C2 model can identify all “persons" positions. However, only
-the EEEA-Net-C2 model could not locate the hidden “person" behind the middle woman in the third column image.
-Additionally, in the fourth column pictures, the EEEA-Net-C2 model was able to identify more “plant pots" than the
-MobileNetv2 and MobileNetv3 models.
-22EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-MobileNetv 3
-EEEA -Net-C2
-Input
-Figure 12: An example of semantic segmentation results of MobileNetv2, MobileNetv3 and EEEA-Net-C2 models.
-MobileNetv 3
-EEEA -Net-C2MobileNetv 2
-Figure 13: An example of human keypoint detection results of MobileNetv2, MobileNetv3 and EEEA-Net-C2 models.
-6.2.2 Semantic segmentation
-The results of visual image segmentation using MobileNetv3 and EEEA-Net-C2 models are shown in Fig. 12. The
-error of the semantic segmentation results can be determined from the pictures of the first column. It was found that
-MobileNetv3 models could segment only “Traffic sign pole". However, the MobileNetv3 model cannot segment the left
-“traffic sign", while the EEEA-Net-C2 model can segment both “pole and sign".
-The pictures in the second column from Fig. 12 depicts that the EEEA-Net-C2 model segmented from the “traffic
-island" was less than the MobileNetv3 model. Next, in the third column, the EEEA-Net-C2 model segmented the
-“footpath" more precisely than the MobileNetv3 model.
-6.2.3 Human keypoint detection
-The human keypoint detection results from the MobileNetv2, MobileNetv3 and EEEA-Net-C2 models are shown in
-Fig. 13. When considering the error from the pictures in the first column, the MobileNetv3 model was found to indicate
-the “left arm" position, while the MobileNetv2 and EEEA-Net-C2 models were able to locate.
-23EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-Figure 14: Comparison of latency between EEEA-Net and other state-of-the-art models on non-GPU processing.
-Only the MobileNetv3 model can pinpoint the “leg" of the person sitting in the second column pictures. However, in the
-third column pictures, the EEEA-Net-C2 model can locate the middle person’s “arms and legs", while the MobileNetv3
-model identifies the person’s wrong location. Additionally, in the fourth column pictures, the EEEA-Net-C2 model
-could locate the “arm and leg" more accurately than the MobileNetv2 and MobileNetv3 models.
-The above data shows that the EEEA-Net-C2 model is accurate as well as inaccurate. The EEEA-Net-C2 model was
-designed and searched with the ImageNet dataset. Thus, the EEEA-Net-C2 model may have errors when used with other
-dataset or tasks. However, the EEEA-Net-C2 model has a performance higher than the MobileNetv2 and MobileNetv3
-models on the same dataset and framework used in the three applications.
-6.3 Mobile Processing
-This appendix measures the performance of our EEEA-Net-C2 model and other state-of-the-art models on the smart-
-phone and only CPU. All trained models with the ImageNet dataset are converted to the PyTorch JIT version to enable
-easy implementation on different platforms.
-Fig. 14 shows the latency performance with 100 images with 224x224 pixels on the Google Pixel 3 XL smartphone
-(blue bars) and Intel i7-6700HQ CPU (red bars) devices with non-GPU resources by DARTSv2, P-DARTS, NASNet,
-ReletiveNAS, ShuffleNetV2, MNASNet 1.0, MobileNetV2, MobileNetV3, and EEEA-Net-C2 models.
-On the Google Pixel 3 XL, the EEEA-Net-C2 model processed each image in 86 milliseconds per image, whereas the
-MobileNetV2 and MobileNetV3 models took 90 and 88 milliseconds, respectively. The EEEA-Net-C2 model has a
-shorter latency time than state-of-the-art models (including DARTSv2, P-DARTS, NASNet, and ReletiveNAS), and
-MobileNets models, primarily models for smartphones, are compared to EEEA-Net-C2.
-Also, on the Intel i7-6700HQ CPU, the latency time of the EEEA-Net-C2 model has shorter latency than state-of-the-art
-models and lightweight models (including MNASNet 1.0, MobileNetV2, and MobileNetV3).
-6.4 NAS-Bench dataset
-Experimental results with CIFAR-10, CIFAR-100, and ImageNet datasets were compared between NAS methods. The
-results obtained from different methods are achieved with different settings such as hyperparameters (e.g., learning rate
-and batch size), data augmentation (e.g., Cutout and AutoAugment). Thus, the comparison may not be fair.
-24EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-0 1 2 3 4 5
-total training time spent (seconds) 1e60.9360.9380.9400.9420.944accuracy
-random
-evolution
-early exit evolution
-Figure 15: Comparison of accuracy between random search, regularised evolution and Early Exit evolution algorithms
-on NAS-Bench-101.
-This section has implemented an Early Exit method to model search from the NAS-Bench datasets, which avoids
-unfair comparisons and provides a uniform benchmark for NAS algorithms. The dataset used in this experiment was
-NAS-Bench-101, NAS-Bench-1Shot1 and NAS-Bench-201.
-6.4.1 NAS-Bench-101
-The NAS-Bench-101 Ying et al. [2019] provides a table dataset of 423,624 unique architectures. These architectures
-have trained and evaluated the CIFAR-10 dataset to allow our work to search and query the mapped performance in the
-dataset in a few milliseconds.
-We have re-implemented model search from the NAS-Bench-101 dataset by using random search, regularised evolution
-and Early Exit evolution algorithms to search and query the performance of the resulting models. We used a re-
-implemented regularised evolution with the Early Exit method by taking population size of 100, a tournament size of
-10, and maximum parameters’ Early Exit ( ) is 25 million.
-The results in Fig. 15 show that our early exit evolution algorithm tends to be higher in accuracy than the regularised
-evolution from 2 million seconds to 5 million seconds. Overall, the regularised evolution algorithm appears to perform
-better than the random search algorithm. However, our early exit evolution tends to outperform both random search and
-regularised evolution algorithms.
-6.4.2 NAS-Bench-1Shot1
-NAS-Bench-1Shot1 Zela et al. [2020] is the benchmark for a one-shot neural architecture search, developed from the
-NAS-Bench-101 search space by tracking the trajectory and performance of the obtained architectures for three search
-spaces: 6,240 architectures for search space 1, 29,160 architectures for search space 2, and 363,648 architectures for
-search space 3.
-Fig. 16 shows the mean performance on validation regret of architectures obtained by random search, regularised
-evolution and Early Exit evolution algorithms. For search space 1, our algorithm achieves validation regret close to
-the regularised evolution algorithm. For search space 2, our algorithm converges better than the regularised evolution
-algorithm. Our algorithm outperforms random search and regularised evolution algorithms for search space 3, the most
-significant (100more architectures than space 1, and 10 more than space 2).
-25EEEA: Early Exit Evolutionary Algorithm Network by Chakkrit Termritthikun, et al.
-104
-105
-estimated wallclock time [s]102
-validation regret
-search space 1
-RS
-RE
-EE
-(a)
-104
-105
-estimated wallclock time [s]102
-validation regret
-search space 2
-RS
-RE
-EE (b)
-104
-105
-estimated wallclock time [s]102
-6×103
-2×102
-validation regret
-search space 3
-RS
-RE
-EE (c)
-Figure 16: Comparison of accuracy between random search (RS), regularised evolution (RE) and Early Exit evolution
-(EE) algorithms on NAS-Bench-1Shot1.
-MethodCIFAR-10 CIFAR-100 ImageNet16-120
-validation test validation test validation test
-ResNet He et al. [2016] 90.83 93.97 70.42 70.86 44.53 43.63
-RSPS Li and Talwalkar [2019] 84.16±1.69 87.66±1.69 45.78±6.33 46.60±6.57 31.09±5.65 30.78±6.12
-Reinforce Williams [1992] 91.09±0.37 93.85±0.37 70.05±1.67 70.17±1.61 43.04±2.18 43.16±2.28
-ENAS Pham et al. [2018] 39.77±0.00 54.30±0.00 10.23±0.12 10.62±0.27 16.43±0.00 16.32±0.00
-DARTS Liu et al. [2018b] 39.77±0.00 54.30±0.00 38.57±0.00 38.97±0.00 18.87±0.00 18.41±0.00
-GDAS Dong and Yang [2019] 90.01±0.46 93.23±0.23 24.05±8.12 24.20±8.08 40.66±0.00 41.02±0.00
-SNAS Xie et al. [2018] 90.10±1.04 92.77±0.83 69.69±2.39 69.34±1.98 42.84±1.79 43.16±2.64
-DSNAS Hu et al. [2020] 89.66±0.29 93.08±0.13 30.87±16.40 31.01±16.38 40.61±0.09 41.07±0.09
-PC-DARTS Xu et al. [2020] 89.96±0.15 93.41±0.30 67.12±0.39 67.48±0.89 40.83±0.08 41.31±0.22
-EA-Net (SO) 91.53±0.00 94.22±0.00 73.13±0.00 73.17±0.00 46.32±0.00 46.48±0.00
-EA-Net (= 0) 88.97±2.48 91.54±2.69 66.84±5.08 67.00±4.90 39.93±5.54 39.27±6.21
-EEEA-Net ( = 0.3) 87.07±1.59 89.76±1.87 64.04±3.21 64.31±3.21 35.42±3.81 34.98±4.13
-EEEA-Net ( = 0.4) 89.91±0.77 92.68±0.69 68.70±1.50 68.65±1.51 41.71±1.58 41.25±1.61
-EEEA-Net ( =0.5) 90.21±0.58 92.83±0.46 69.15±1.36 68.95±1.25 42.14±1.14 41.98±1.22
-Optimal 91.61 94.37 73.49 73.51 46.77 47.31
-Table 9: Comparison of a single objective and multi-objective evolution algorithm with the 8 NAS methods provided by
-NAS-Bench-201 benchmark. Optimal shows the best architecture in the search space.
-6.4.3 NAS-Bench-201
-NAS-Bench-201 Dong and Yang [2020] is an extension of NAS-Bench-101, which extends different search spaces,
-and it has a wide range of datasets, including CIFAR-10, CIFAR-100, and ImageNet-16-120. It contains 15,625
-architectures by five operations, and 6-dimensional vectors indicate the operation in the cell. All architecture evaluated
-performance by validation and test sets on CIFAR-10, CIFAR-100, and ImageNet-16-120.
-We compare our Early Exit Evolution Algorithm or EEEA-Net ( = 0.3, 0.4 and 0.5) with the single objective evolution
-algorithms (SO) and multi-objective evolution algorithms ( = 0). The hyper-parameters for this search process were
-defined as the generations of EA equal to 10 generations with 100 populations by retaining a probability of 0.5, a
-mutation probability of 0.1, and Early Exit is the maximum number of parameters equal to 0.3, 0.4 and 0.5 million.
-The results are shown in Table 9, our EEEA-Net ( = 0.4 and 0.5) outperforms EEEA-Net ( = 0 and 0.3). However,
-the EA-Net (SO) using accuracy as the optimisation objective performed better than all EEEA-Nets.
-Furthermore, when we compared our EEEA-Net ( = 0.5) with 8 NAS methods, including RSPS Li and Talwalkar
-[2019], Reinforce Williams [1992], ENAS Pham et al. [2018], DARTS Liu et al. [2018b], GDAS Dong and Yang
-[2019], SNAS Xie et al. [2018], DSNAS, and PC-DARTS Xu et al. [2020], we found that EEEA-Net ( = 0.5) has an
-accuracy was higher than all other NAS method except for the Reinforce method.
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-This is a preprint: it has been accepted  for publication  in the Conference: 2020 6th IEEE International 
-Conference on Network Softwarization (NetSoft) . 
-• DOI: 10.1109/NetSoft48620.2020.9165337  
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- 
- Detection  of Insider  Threats  using  Artificial  
-Intelligence  and Visualisation  
-Vasileios  Koutsouvelis∗, Stavros  Shiaeles†, Bogdan  Ghita‡, Gueltoum  Bendiab‡ 
-∗Open  University  of Cyprus,  33 Yiannou  Kranidioti  Ave.,  Latsia,  Nicosia,  Cyprus  
-[email protected]  
-†Network  and Security  Research  Group  (NSRG),  School  of Computing,  
-University  of Portsmouth,  Winston  Churchill  Avenue,  Portsmouth,  PO1 2UP,  Hampshire,  UK 
-[email protected]  
-‡ Centre  for Security,  Communications  and Networks  Research  (CSCAN),  
-School  of Computing  and Mathematics,  Plymouth  University,  Drake  Circus,  Plymouth  PL4 8AA,  UK 
-[email protected],  [email protected]  
- 
- 
-Abstract —Insider threats are one of the most damaging risk  
-factors for the IT systems and infrastructure of a company or an  
-organization; identification of insider threats has prompted the  
-interest of the world academic research community, with several  
-solutions  having  been  proposed  to alleviate  their  potential  impact.  
-For th e implementation of the experimental stage described in  this 
-study,  the Convolutional  Neural  Network  (from  now on CNN)  
-algorithm was used and implemented via the Google TensorFlow  
-program, which was trained to identify potential threats from  
-images produced by the available dataset. From the examination  
-of the images that were produced and with the help of Machine  
-Learning,  the question  whether  the activity  of each  user is 
-classified as “malicious” or not for the Information System was  
-answere d. 
-Index Terms —Threats, visualization, security, artificial intelli - 
-gence,  machine  learning  
- 
-I. INTRODUCTION  
-Computers nowadays are used in every human activity and  
-all kinds of operations, from residential to commercial and  from  
-basic  service  provisioning  to research.  Beyond  their benefit,  
-the risks  and cases  of deliberate  or accidental  de struction, 
-tampering or unauthorised use of data and computer  resources,  
-in general,  are increasing.  The consequences  of possible 
-tampering, leakage, or misuse of data can lead not  only to 
-significant damage and costs but also risks for the  protection  of 
-the human  rights  of individuals  [1]. In the current  security 
-monitoring landscape, artificial neural networks play  an 
-extremely important role in the prevention and handling of  
-internal threats [2] and alleviating the risks associated with  
-information syste ms infrastructures. While a typical infras - 
-tructure would have a level of protection against an external  
-attack/threat, an internal threat refers to a person who may  have 
-privileged access to classified, sensitive, or proprietary  data and 
-uses this advant age to remove information from an  organization  
-and transfer  it to unauthorised  external  users.  Such attackers 
-may include the users of the company, who can  bypass the 
-control procedures for access to classified data, and  the users  
-who gain access  to user accounts  with more  rights  in relation to these, which they already have. The purpose of  
-this survey was to answer the question of whether Artificial  
-Intelligence  can be successfully  used to detect  malicious  
-activity. The answer to this question went through three (3)  
-steps: (a) collecting, processing, and classifying the data of the  
-users tested; (b) visualizing the extracted data; (c) categorize  
-the behaviour  as malicious  or normal.  
-II. RELATED  WORK  
-The detection  of malicious  activity  with the help of Artificial  
-Intelligence  has been  a matter  of concern  to scholars,  who have  
-occasionally dealt with this issue, using different approaches.  
-For instance, X. Ren, L. Wang, [3] presented a hybrid insider  
-threat detection system, consisting of data processing, entity  
-portrait,  rule matching  as well as iterative  attention.  Based  
-on the results  of the experiments,  the authors  claim  that the 
-propose d system  provides  a higher  detection  rate and better 
-timeliness since it incorporates multiple complementary  
-detection  techniques  and components.  In [4], Sajjanhar,  Atul et 
-al proposed an image -based feature representation of the daily  
-resource usage pattern of users in the organization. authors  
-reported  an overall  accuracy  of 99% when  compared  with 
-other techniques. In another recent study [5], Kim, Junhong et  
-al introduced an insider threat detection framework based on  
-user behaviour modelling and anomaly detection algorithms  
-and they support those experimental results  indicate that the  
-proposed framework can work well for imbalanced datasets in  
-which  there  are only a few insider threats  and where  no domain  
-experts’  knowledge  is provided.  In the same  context,  Hu, Teng  
-et al [6] proposed a continuous identity authentication method  
-based on mouse dynamic behaviour and deep learning to solve  
-the insider  threat  attack  detection  problem  and they claim  that 
-the experimental results showed that the proposed method  could 
-identify the user’s identities in seconds and has a lower  false  
-accep t rate (FAR)  and false  reject  rate (FRR).  
-Tuor, Kaplan and Hutchinson [7] referred to a system of  
-profound knowledge for filtering log files’ data and analysing  
-them.  According  to the writers,  an internal  threat  behaviour  varies widely, so the researcher does not attempt to formulate  
-the pattern  of behaviour  which  is a threat.  Instead,  new variants  
-of Neural  Networks  (DNN)  and Recurring  Neural  Networks  
-(RNNs)  are trained  to recognize  the activity  that is typical  
-for each user on a network.  At the same  time,  these Neural 
-Networks assess whether the behaviour of the  user is normal or 
-suspicious. The authors note that detecting  malicious cases is 
-particularly difficult because attackers often  try to imitate  the 
-typical  behaviour  of a normal  user.  In another  study, Sanzgiri 
-and Dasgupta [8] presented the techniques that  have been 
-developed to detect internal threats referring to the  researchers 
-and their techniques. In particular, the objective of  this paper is 
-to present a categorization of the techniques used  by 
-researchers (Hu, Giordano, Kandias, Maybury, Greitzer,  
-Eldardiry) to deal with insider threats. Finally, the researchers  
-remarked  that one of the main  reasons  why it is still difficult  
-to detect  attacks  by internal  users  is the lack of sufficient  
-real available  data in order  to build  and test models  and 
-mechanisms  for detecting  internal  threats.  
-Breier  and Branisova  [9] proposed  a threat  detection  method  
-based  on data mining  techniques  for analysing  system  log 
-files (log analysis).  Their  approach  was based  on Apache  
-Hadoop,  which  allows  the processing  of large  volumes  of data 
-in a parallel  way.  The method  detects  new types  of viola -
-tions  without  further  human  intervention,  while  the overall  
-error  reaches  a value  below  10%.  Legg,  Buckley,  Goldsmith  
-and Creese  (2015)  [10] proposed  Corporate  Insider  Threat  
-Detection  (CITD),  a corporate  threat  detection  system  which  
-incor porates  technical  and behavioural  activities  for the evalu - 
-ation of threats caused by individuals. In particular, the system  
-recognised  the users  and the role-based  profiles  and measured  
-how they deviate  from  their observed  behaviour  in order  to 
-estimate  the potential  threat  that a set of user activities  can 
-cause. Some  other  studies have  used approaches  [11] based  on 
-graphs  to find malicious  cases  in structural  data models  that 
-represent  an internal  threat  activity  that is looking  for activities  
-that display  similarities  to normal  data transactions  but are 
-structurally  different  from  them.  Others  [12] have  suggested  
-approaches  that combine  Structural  Anomaly  Detection  - SA. 
-Despite  the work  to date,  the challenge  to holistically  
-observe  and analyse  user and application  behaviour  remains  a 
-current  one, due to its volume  and complexity.  The benefits  
-of AI-based  solution  are obvious  when  faced  with the large  
-amounts  of data collected,  but interpreting  the data and results  
-demands  further  research.  
-III. PROPOSED  APPROACH  
-As identified  in the previous  section,  existing  research  
-demonstrated the efficiency of machine learning approaches,  
-but also exposed  their limitations  in segregating  the complexity  
-of user behaviour  into normal  and malicious.  To account  
-for this complexity,  we apply  the CNN  algorithm  on user 
-interaction data, as reflected through the log files collected  from  
-individual  users.  Unlike  other  studies  focusing  on domain  
-knowledge  to detect  malicious  behaviours  through  the use of 
-specific  structural  anomaly  detection  [13],  our proposed  approach  focuses  exclusively  on the behaviour  of each user 
-of the Information  Technology  System.  In addition  to the 
-standard approach of log data collection [10, 14], the method  
-also considers  the organizational  role for each user when  estab - 
-lishing the behaviour profile, which improved significantly the  
-accuracy of the method. For example, the “malicious” activity  
-of a user, who holds an IT Admin position in the organization,  
-may justify  - to a certain  extent  - this activity.  
-In the context  of internal  threats,  the method  aims  to 
-discriminate between legitimate and malicious behaviour by  
-investigating  the differences  in the associated  visualisation  
-maps.  For each user,  the approach  creates  an image  that 
-depicted his/her activity and behaviour, as emerged from their  
-interaction with various information systems. While the result - 
-ing images may appear visuall y different, they were processed  
-through  a machine  learning  algorithm  in order  to automatically  
-recognize  which  subset  of the users  appear  to exhibit  malicious  
-behaviour (and therefore posing a threat for the respective  
-information  systems)  and which  are legitimate/benign  ones.  
-Two stages are critical for the success of the method: the  
-input data and the processing method. Given the proposed  
-approach  is aiming  to provide  a holistic  view  of the user 
-interactions,  the most  appropriate  method  was considered  to 
-be converting log events into a visual representation. A full  
-overview of the process is provided in the following section,  
-including the attempts to highlight the most relevant features  
-through the chosen visualisation. The second critical s tage,  
-information  processing,  was biased  by the choice  of input  data. 
-Research undertaken in recent years demonstrated that  
-Convolutional Neural Networks are indeed extremely efficient  
-at image analysis, as shown by [15]. However, they have also  
-proved the ir efficiency in the security area. Given their ability  
-to deal with complex relationship, CNNs have been applied  
-from basic security problems, as in [16] which introduced a  
-CNN -based generic detection engine, to [17] for analysis of  
-encrypted  content.  
-The approach presented in this paper follows from the mal - 
-ware  classification  method  from  Wang  et al. [18] of converting  
-the data analysis  task into an image  recognition  one. Unlike  
-[18] and [19],  which  look at either  malware  or network  activity  
-to detect  attacks,  this paper  aims  to extend  the analysis  into 
-the logging footprint to detect malicious vs normal behaviour.  
-As highlighted in previous studies, the challenge is to ensure  
-that sufficient input data is available for the method to be  
-successful,  and the transformation applied makes it compatible  
-with the image  analysis  task. 
-A. Methodology  
-The input data was the Insider Threat Test Dataset from  
-CERT, which is part of the Institute of Software Engineering  
-(SEI).  The file included  log files,  CSV  type,  which  recorded  
-an activity covering eighteen (18) months, collected between  
-01.01.2010  and 31.05.2011.  Through  these  files and after 
-analysis and pro cessing, it was attempted to present an image  
-of the Information System and to analyse the behaviour of  users  
-identified  as malicious.  For each user,  the inputs  included  login records, files/documents used or opened, emails sent and  
-received, web browsing history, devices used, and user role  
-within  the organization.  
-The steps  we followed  to complete  the process  and draw  
-our conclusions  were  1) data sharing  and creation  of files 
-based on the data of the user under consideration, 2) importing  
-the data files we created in the ”D3.js” library, selecting an  
-appropriate  image  creation  plan,  examining  the application  
-library’s patterns and creating images of the user i n question  
-that included  his/her  activity  during  each day, 3) creating  
-images, 4) implementing and training the CNN algorithm in  
-Tensorflow program and examining user behaviour, which we  
-have described as ”normal” or ”malicious”; and 5) drawing  
-conclusions . 
-1) First Stage: In the first stage of pre-processing , the raw  
-input files were parsed to classify the log files from fifteen  users 
-according to the user role (salesman, IT admin, electrical  
-engineer, mechanical engineer, administrator, production line  
-worker,  computer  scientist,  and software  quality  engineer).  
-First, log files were parsed for each of the 15 users to create  
-separate profiles consisting of three files (file.csv, email.csv,  
-http.csv), which included the complete activity. In the second  
-step, the profile files were compared against a number of rules  
-that defined threats and malicious behaviour. In the website  
-category,  we chose  terms  that were  associated  with social  
-networks, work  search sites, malware, and file sharing. The  
-parsing scripts also searched for attached files, which were  
-divided into three size intervals, defining small [50K B -100K  
-B] medium  [100KB -200KB]  and large  files (over  200KB).  
-In parallel, we also parsed the p rofile files for terms associ - 
-ated with malware, such as Keylogger, files that may have  been  
-leaked  to the Internet,  and files that may have  been  distributed 
-over the Internet. The result of the pre-processing  was a set of 
-three files per user, which had the following  structure: date, 
-user (username), host (from which the action  was carried out), 
-keyword (defining the type of threat), threat  index (e ach 
-specific threat was assigned a numerical category)  which files 
-contained the respective threats. The activity was  classified  
-using  a separate  record  field which  defined  a specific  type of 
-[illegal] activity. At a subsequent stage, the field was  
-transco ded to a unique colour  for visualization of the images  
-associated  with the user activity  map.  The collected  content  
-for the users was then aggregated into weekly and monthly  
-activity  for long-term analysis.  
-2) Second  Stage:  In the second  stage  of the experiment,  
-the numerical  input  was processed  through  the Java D3.js  
-library to visualize and produce images that depict the activity  
-of each user. Concerning D3.js is an open -source  JavaScript  
-library, used for document and data handling, which is based  
-on web templates. It is used through upgraded web browsers,  
-combining powerful data visualization methods and elements.  
-Large  datasets  can be easily  linked  to SVG  objects  using  simple 
-D3.js functions to create rich text and graphics di agrams.  With  
-a minim al resource  burden  on a computer  system,  D3.js  is 
-extremely  fast, supporting  large  data sets and dynamic  
-interactions  through  static  or moving  images.  As mentioned above, the illegal activity was colour -coded for  
-visual processing as follows: blue for social and professional  
-interaction, such as job search and social networking sites, red  
-for sharing site file -sharing websites, pink for email threats,  
-green for file threats,  yellow  for 50-100 KB email  attachments,  
-orange  for 100-200 KB email  attachments,  and brown  for 
-> 200KB email attachments. Using this coding, the user data  
-was converted to an hourly resolution heatmap, summarising  
-week -long and month -long data; this allowed a harmonised  
-view  of the data for the two timeframes.  This process  aimed  
-to convert  the numerical  data into a visual  representation  and 
-characterization  of the activity  to determine  whether  it is 
-malicious  or normal.  
-The result  is a two-dimension  heatmap,  colour -coded  as 
-described  above,  with the days of the month  on the vertical  
-and the hours of the day horizontally. The dimensions of the  
-design area were determined according to the size of the grid  
-that was set. The result was the creation of a space consisting  
-of hourly  activity  squares,  each of which  was coloured  with 
-the dominant  activity  identified  during  that respective  timeslot.  
-Following  the generation  of the activity -based  heatmap,  the 
-full dataset included a total of 1199 images; these were  
-manually labelled as normal or malicious. The selection was  
-based  on specific  criteria,  indicative  of the density  of the 
-activity  at specific  time intervals,  its colours,  and the time 
-of day. Following manual analysis, 769 images were labelled  
-as containing malicious activity and 430 images were labelled  
-as normal.  Indicatively,  Figure  1 presents  four such images.  
- 
- 
- 
-Fig. 1. Display  of user’  normal  and malicious  activity  weekly  and monthly  
- 
- 
-Fig. 2. Schematic  layout  of the CNN  algorithm  implemented  
- 
-3) Third  Stage:  In the third  stage  of the experiment,  
-Google’s Tensorflow program was used to design a six -layer  
-CNN  that would  recognize  whether  the image  was classified  
-as normal  or malicious.  CNN  is a class  of forward -facing  
-artificial  neural  networks  and has been  successfully  applied  
-to the analysis and recognition of visual images, videos, and  
-natural language processing systems. It also uses relatively  
-little processing compared to other image sorting algorithms.  
-This means that the network easily learns filters made using  
-traditiona l algorithms.  It is also known  as an invariant  artificial  
-neural network, based on the weight’s  architecture. Finally, in  
-the previous  step one thousand  one hundred  ninety -nine (1199)  
-images were produced, of which - as reported - four hundred  
-and thirty (430) were assessed as containing normal activity.  
-For this reason, a corresponding number of malicious images  
-were selected. Finally, eight hundred and sixty (860) images  
-were used for  this stage, of which eight hundred forty (840)  
-were  the training  data images  as follows:  
-1) Training  Data : 80% of training  images  were  used.  
-2) Validation Data : 20% of training images were used for  
-validation.  
-Figure  2 shows  the resulting  implementation.  
- 
-B. Evalu ation  
-In order to ensure a balanced dataset, the 769 malicious  
-activity images were reduced through random selection to 430  
-images, matching the number of normal activity images. The  
-resulting dataset had 860 samples, including an equal number  
-of normal and malicious activity samples; the dataset was split  
-as follows: 20 images were set aside for validation and the  
-remaining 840 images were split 80% training (672 images)  
-and 20% testi ng (168 images). The validation images were  also 
-selected  in a balanced  fashion,  with an equal  number  
-(10) of normal  and malicious  activity  samples.  The forecasting  
-of the results was successful, with a proportion that reached  
-100%;  the CNN  algorithm  was traced  graphically  to 
-illustrate training accuracy, validation accuracy, and cost. 
-Below  are presented  the three  training  exercises  (training  
-accuracy,  validation accuracy, cost) and the implementation graph of the  
-CNN  algorithm.  
-According to the graphs in Figure 3, training accuracy starts  
-at a value close to 0.450, i .e., 45%, and has an increasing value  
-to reach a rate close to 100%. Validation accuracy (F igure 3)  
-starts from a value close to 0.400, i.e., 40%, and has a rising  
-value  to reach  a rate close  to 90%.  While  the cost starts  from  a 
-value close to 0.700, i.e., 70% and has a decreasing value to  
-arrive  at a value,  which  is close  to 0. 
-IV. CONCLUSION  
-The purpose  of this study  was to investigate  the feasibility  of 
-using machine learning techniques to detect malicious activity  
-by converting activity reports into a visual representation. The  
-answer to this question has gone through three stages: a) the  
-collection,  processing,  and classification  of the data of the users  
-under consideration, b) the visualization of the extracted data,  
-and c) the use of the CNN  algorithm  to classify  behaviour  
-into malicious  or normal.  The algorithm  was trained  and tested 
-using a dataset of 860 created images, including both  malicious 
-and normal activity. Our conclusion  is that,  with the 
-methodology used, the malicious activity of the users  of the 
-information  system  was achieved.  The forecasting  of the 
-results was successful , with a percentage that  reached  100%.  
-Table  I is a concise  table  showing  the results  of some  internal  
-recognition  methods,  both in terms  of validation  accuracy  and 
-some of their comparisons.  
-In conclusion,  it should  be noted  that the characterization  
-of a user’s behaviour as malicious is also dependent on the  
-Security Policy that the Company or Organization adopts to  
-protect  its Information  System.  For the analysis,  one of the 
-sets polices was that visiting social networking sites or job  
-search  websites  is falls outside  normal  activity  and should  
-be categorised  as malicious. In addition, an important role in  
-characterising  a user’s  activity  is played  by the position  held 
-in the organization and the set policy and associated analysis  
-should  also consider  this. 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 3. Graphic  representation  of Training  Accuracy,  Validation  Accuracy  and Cost 
-TABLE  I 
-COMPARISON OF OUR MET HOD  WITH  OTHERS  
- 
-Study  Accuracy    Comparison  
-Proposed  method  Testing  Accuracy:  100%  Mechanical  learning  (machine  learning)  and training  of the CNN  algorithm  for 
-Training  Accuracy:100%  
-Validation  Accuracy:90.6%  
-Cost:  0.582  the implementation  and categorization  of the user’s  behaviour  in normal  and 
-malicious.  
- 
-W. Eberle  et al [11] Testing  Accuracy:  95% In this method  a graphical  theoretical  approach  was used to detect  malicious  user 
-behaviour  in an Information  System  
-O. Brdiczka  et al [12] Server  Eitrigg:82.74%  This study  proposes  an approach  combining  structural  anomaly  detection  (SA)  
- Server  Cenarion  Circle:89.09%  from  social  networks  and information  networks  as well as the psychological  
- Server  Bleeding  Hollow:79.84%  profiling  (PP) of individuals.  
-W. T. Young  et al [13] Indicators:87.4%  This study  focuses  on the awareness  of domain  knowledge  to detect  malicious  
- Anomalies:97.9%  behaviour  through  the use of specific  SAs algorithms.  
- Scenarios:80.6%   
-J. Breier  et al [14]  The error  rate of the method  used was less than 10%.  In the method  we used,  
-  the error  rate approaches  zero (0). 
-P. A. Legg  et al [10]  The method  used was based  on collecting  log data,  by building  a profile  for each 
-  user and his / her property.  In the method  we used,  the user property  was not 
-  evaluated.  
-X.Ren,  L.Wang  [3]  The method  proposes  a hybrid  intelligent  system  for insider  threat  detection  that 
-  aims  to realize  more  effective  detection  of security  incidents  by incorporating  
-  multiple  complementary  detection  techniques,  such as entity  portrait,  rule match - 
-     ing and iterative  attention.  In our method,  only the user activity  was evaluated  
-A.Sajjanhar  et al [4] Accuracy  99% The method  proposes  an image -based  feature  representation  of the daily  resource  
-  usage  pattern  of users  in the organization.  Classification  models  are applied  to 
-  the representative  images  to detect  anomalous  behaviour  of insiders.  The images  
-  are classified  too malicious  and no malicious  
-J.Kim  et al [5] Accuracy  > 90%  The method  proposes  insider -threat  detection  methods  based  on user behaviour  
-  modelling  and anomaly  detection  algorithms.  Experimental  results  show  that the 
-  proposed  framework  can work  reasonably  well to detect  insiders’  malicious  
-  behaviours . Although  the proposed  framework  was empirically  verified,  there  are 
-  some  limitations  in the current  research,  which  led the authors  to future  research  
-  directions  
- 
-ACKNOWLEDGMENT  
-This project  has received  funding  from  the Euro - 
-pean  Union’s  Horizon  2020  research  and innovation  
-programme  under  grant  agreement  no. 786698.  The work  re- 
-flects  only the authors’  view  and the Agency  is not responsible  
-for any use that may be made  of the information  it contains.  
- 
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-866. 

txt/2109.04223.txt

@@ -1,998 +0,0 @@
-Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-KELM: K NOWLEDGE ENHANCED PRE-TRAINED LAN-
-GUAGE REPRESENTATIONS WITH MESSAGE PASSING
-ONHIERARCHICAL RELATIONAL GRAPHS
-Yinquan Lu1,5Haonan Lu2yGuirong Fu3Qun Liu4
-Huawei Technologies Co., Ltd.1OPPO Guangdong Mobile Telecommunications Co., Ltd.2
-ByteDance3Huawei Noah’s Ark Lab4Shanghai AI Laboratory5
-[email protected], [email protected]
-[email protected], [email protected]
-ABSTRACT
-Incorporating factual knowledge into pre-trained language models (PLM) such as
-BERT is an emerging trend in recent NLP studies. However, most of the existing
-methods combine the external knowledge integration module with a modified
-pre-training loss and re-implement the pre-training process on the large-scale
-corpus. Re-pretraining these models is usually resource-consuming, and difficult
-to adapt to another domain with a different knowledge graph (KG). Besides,
-those works either cannot embed knowledge context dynamically according to
-textual context or struggle with the knowledge ambiguity issue. In this paper, we
-propose a novel knowledge-aware language model framework based on fine-tuning
-process, which equips PLM with a unified knowledge-enhanced text graph that
-contains both text and multi-relational sub-graphs extracted from KG. We design
-a hierarchical relational-graph-based message passing mechanism, which allows
-the representations of injected KG and text to mutually update each other and can
-dynamically select ambiguous mentioned entities that share the same text1. Our
-empirical results show that our model can efficiently incorporate world knowledge
-from KGs into existing language models such as BERT, and achieve significant
-improvement on the machine reading comprehension (MRC) tasks compared with
-other knowledge-enhanced models.
-1 I NTRODUCTION
-Pre-trained language models benefit from the large-scale corpus and can learn complex linguistic
-representation (Devlin et al., 2019; Liu et al., 2019b; Yang et al., 2020). Although they have achieved
-promising results in many NLP tasks, they neglect to incorporate structured knowledge for language
-understanding. Limited by implicit knowledge representation, existing PLMs are still difficult to
-learn world knowledge efficiently (Poerner et al., 2019; Yu et al., 2020). For example, hundreds
-of related training samples in the corpus are required to understand the fact “ banmeans an official
-prohibition or edict against something” for PLMs.
-By contrast, knowledge graphs (KGs) explicitly organize the above fact as a triplet “(ban, hypernyms,
-prohibition)” . Although domain knowledge can be represented more efficiently in KG form, entities
-with different meanings share the same text may happen in a KG (knowledge ambiguity issue). For
-example, one can also find “(ban, hypernyms, moldovan monetary unit)” in WordNet (Miller, 1995).
-Recently, many efforts have been made on leveraging heterogeneous factual knowledge in KGs to
-enhance PLM representations. These models generally adopt two methods: (1). Injecting pre-trained
-entity embeddings into PLM explicitly, such as ERNIE (Zhang et al., 2019), which injects entity
-embeddings pre-trained on a knowledge graph by using TransE (Bordes et al., 2013). (2). Implicitly
-This work is done when Yinquan Lu, Haonan Lu and Guirong Fu work at Huawei Technologies Co., Ltd.
-yCorresponding author
-1Words or phrases in the text corresponding to certain entities in KGs are often named “entity mentions” .
-While entities in KGs that correspond to entity mentions in the text are often named “mentioned entities”
-1arXiv:2109.04223v2  [cs.CL]  5 May 2022Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-learning factual knowledge by adding extra pre-training tasks such as entity-level mask, entity-based
-replacement prediction, etc. (Wang et al., 2020c; Sun et al., 2020). Some studies use both of the
-above two methods such as CokeBERT (Su et al., 2020).
-However, as summarized in Table 4 of Appendix, most of the existing knowledge-enhanced PLMs
-need to re-pretrain the models based on an additional large-scale corpus, they mainly encounter
-two problems below: (1) Incorporating external knowledge during pretraining is usually resource-
-consuming and difficult to adapt to other domains with different KGs. By checking the third column
-of Table 4 in Appendix, one can see that most of the pretrain-based models use Wiki-related KG as
-their injected knowledge source. These models also use English Wikipedia as pre-training corpus.
-They either use an additional entity linking tool (e.g. TAGME (Ferragina & Scaiella, 2010)) to align
-the entity mention in the text to a single mentioned entity in a Wiki-related KG uniquely or directly
-treat hyperlinks in Wikipedia as entity annotations. These models depend heavily on the one-to-one
-mapping relationship between Wikipedia corpus and Wiki-related KG, thus they never consider
-handling knowledge ambiguity issue. (2) These models with explicit knowledge injection usually use
-algorithms like BILINEAR (Yang et al., 2015) to obtain pre-trained KG embeddings, which contain
-information about graph structure. Unfortunately, their knowledge context is usually static and cannot
-be embedded dynamically according to textual context.
-Several works (Qiu et al., 2019; Yang et al., 2019) concentrate on injecting external knowledge based
-on fine-tuning PLM on downstream tasks, which is much easier to change the injected KGs and adapt
-to relevant domain tasks. They either cannot consider multi-hop relational information, or struggle
-with knowledge ambiguity issue. How to fuse heterogeneous information dynamically based on the
-fine-tuning process on the downstream tasks and use the information of injected KGs more efficiently
-remains a challenge.
-Figure 1: Unified Knowledge-enhanced Text Graph (UKET)
-consists of three parts corresponding to our model: (1) KG
-only part, (2) Entity link to token graph, (3) Text only graph.To overcome the challenges mentioned
-above, we propose a novel frame-
-work named KELM , which injects
-world knowledge from KGs during
-the fine-tuning phase by building a
-Unified Knowledge-enhanced Text
-Graph (UKET) that contains both in-
-jected sub-graphs from external knowl-
-edge and text. The method extends
-the input sentence by extracting sub-
-graphs centered on every mentioned
-entity from KGs. In this way, we can
-get a Unified Knowledge-enhanced
-Text Graph as shown in Fig. 1, which is
-made of three kinds of graph: (1) The
-injected knowledge graphs, referred to
-as the “ KG only ” part; (2) The graph
-about entity mentions in the text and mentioned entities in KGs, referred to as the “ entity link to
-token ” part. Entity mentions in the text are linked with mentioned entities in KGs by string matching,
-so one entity mention may trigger several mentioned entities that share the same text in the injected
-KGs (e.g. “Ford” in Fig. 1); (3) The “ text only ” part, where the input text sequence is treated as a
-fully-connected word graph just like classical Transformer architecture (Vaswani et al., 2017).
-Based on this unified graph, we design a novel Hierarchical relational-graph-based Message Passing
-(HMP) mechanism to fuse heterogeneous information on the output layer of PLM. The implemen-
-tation of HMP is via a Hierarchical Knowledge Enhancement Module as depicted in Fig. 2, which
-also consists of three parts, and each part is designed for solving the different problems above: (1)
-For reserving the structure information and dynamically embedding injected knowledge, we utilize
-a relational GNN (e.g. rGCN (Schlichtkrull et al., 2017)) to aggregate and update representations
-of extracted sub-graphs for each injected KG (corresponding to the “KG only” part of UKET). All
-mentioned entities and their K-hop neighbors in sub-graphs are initialized by pre-trained vectors
-obtained from the classical knowledge graph embedding (KGE) method (we adopt BILINEAR here).
-In this way, knowledge context can be dynamically embedded, the structural information about the
-graph is also kept; (2) For handling knowledge ambiguity issue and selecting relevant mentioned
-entities according to the input context, we leverage a specially designed attention mechanism to
-2Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-Figure 2: Framework of KELM (left) and illustrates how to generate knowledge-enriched token
-embeddings (right).
-weight these ambiguous mentioned entities by using the textual representations of words/tokens to
-query the representations of their related mentioned entities in KGs (corresponding to the “entity
-link to token” graph of UKET). The attention score can help to select knowledge according to the
-input sentence dynamically. By concatenating the outputs of this step with the original outputs
-of PLM, we can get a knowledge-enriched representation for each token; (3) For further interac-
-tions between knowledge-enriched tokens, we employ a self-attention mechanism that operates on
-the fully-connected word graph (corresponding to the “text only” graph of UKET) to allow the
-knowledge-enriched representation of each token to further interact with others.
-We conduct experiments on the MRC task, which requires a system to comprehend a given text
-and answer questions about it. In this paper, to prove the generalization ability of our method,
-we evaluate KELM on both the extractive-style MRC task (answers can be found in a span of the
-given text) and the multiple-response-items-style MRC task (each question is associated with several
-choices for answer-options, the number of correct answer-options is not pre-specified). MRC is a
-challenging task and represents a valuable path towards natural language understanding (NLU). With
-the rapid increment of knowledge, NLU becomes more difficult since the system needs to absorb new
-knowledge continuously. Pre-training models on large-scale corpus is inefficient. Therefore, fine-
-tuning the knowledge-enhanced PLM on the downstream tasks directly is crucial in the application.2
-2 R ELATED WORK
-2.1 K NOWLEDGE GRAPH EMBEDDING
-We denote a directed knowledge graph as G(E;R), whereEandRare sets of entities and relations,
-respectively. We also define Fas a set of facts, a fact stored in a KG can be expressed as a triplet
-(h;r;t )2F, which indicates a relation rpointing from the head entity hto tail entity t, where
-h;t2E andr2R . KGE aims to extract topological information in KG and to learn a set of
-low-dimensional representations of entities and relations by knowledge graph completion task (Yang
-et al., 2015; Lu & Hu, 2020).
-2.2 M ULTI -RELATIONAL GRAPH NEURAL NETWORK
-Real-world KGs usually include several relations. However, traditional GNN models such as
-GCN (Kipf & Welling, 2017), and GAT (Veli ˇckovi ´c et al., 2018) can only be used in the graph
-2Code is available at here.
-3Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-with one type of relation. (Schlichtkrull et al., 2017; Haonan et al., 2019) generalizes traditional
-GNN models by performing relation-specific aggregation, making it possible to encode relational
-graphs. The use of multi-relational GNN makes it possible to encode injected knowledge embeddings
-dynamically in SKG and CokeBERT.
-2.3 J OINT LANGUAGE AND KNOWLEDGE MODELS
-Since BERT was published in 2018, many efforts have been made for further optimization, basically
-focusing on the design of the pre-training process and the variation of the encoder. For studies of
-knowledge-enhanced PLMs, they also fall into the above two categories or combine both of them
-sometimes. Despite their success in leveraging external factual knowledge, the gains are limited by
-computing resources, knowledge ambiguity issue, and the expressivity of their methods for the fusion
-of heterogeneous information, as summarized in Table 4 of Appendix and the introduction part.
-Recent studies notice that the architecture of Transformer treats input sequences as fully-connected
-word graphs, thus some of them try to integrate injected KGs and textual context into a unified
-data structure. Here we argue that UKET in our KELM is different from the WK graph proposed
-in CoLAKE/K-BERT. These two studies heuristically convert textual context and entity-related
-sub-graph into input sequences, both entities and relations are treated as input words of the PLM,
-then they leverage a Transformer with a masked attention mechanism to encode those sequences
-from the embedding layer and pre-train the model based on the large-scale corpus. Unfortunately, it
-is not trivial for them to convert the second or higher order neighbors related to textual context (Su
-et al., 2020), the structural information about the graph is lost. UKET differs from the WK graph
-of CoLAKE/K-BERT in that, instead of converting mentioned entities, relations, and text into a
-sequence of words and feeding them together into the input layer of PLM (they unify text and KG into
-a sequence), UKET unifies text and KG into a graph. Besides, by using our UKET framework, the
-knowledge fusion process of KELM is based on the representation of the last hidden layer of PLM,
-making it possible to directly fine-tune the PLM on the downstream tasks without re-pretraining the
-model. SKG also utilizes relational GNN to fuse information of KGs and text representation encoded
-by PLM. However, SKG only uses GNN to dynamically encode the injected KGs, which corresponds
-to part one of Fig. 1. Outputs of SKG are made by directly concatenating outputs of graph encoder
-with the outputs of PLM. It cannot select ambiguous knowledge and forbids the interactions between
-knowledge-enriched tokens corresponding to part two and part three of Fig. 1, respectively. KT-NET
-uses a specially designed attention mechanism to select relevant knowledge from KGs. For example,
-it treats all synsets of entity mentions within the WN183as candidate KB concepts. This limits the
-ability of KT-NET to select the most relevant mentioned entities4. Moreover, the representations
-of injected knowledge are static in KT-NET, they cannot dynamically change according to textual
-context, the information about the original graph structure in KG is also lost.
-3 M ETHODOLOGY
-The architecture of KELM is shown in Fig. 2. It consists of three main modules: (1) PLM Encoding
-Module; (2) Hierarchical Knowledge Enhancement Module; (3) Output Module.
-3.1 PLM E NCODING MODULE
-This module utilizes PLM (e.g.BERT) to encode text to get textual representations for pas-
-sages and questions. An input example of the MRC task includes a paragraph and a ques-
-tion with a candidate answer, represented as a single sequence of tokens of the length n:
-T=f[CLS];Q;(A);[SEP ];P;[SEP ]g=ftign
-i=1, whereQ,AandPrepresent all tokens for ques-
-tion, candidate answer and paragraph, respectively5.[SEP ]and[CLS]are special tokens in BERT
-and defined as a sentence separator and a classification token, respectively. i-th token in the sequence
-is represented by ~ht
-i2Rdt, wheredtis the last hidden layer size of used PLM.
-3A subset of WordNet.
-4Refer the example given in the case study of KT-NET, the most relevant concept for the word “ban” is
-“forbidding_NN_1” (with the probability of 86.1%), but not “ban_NN_4”.
-5Depending on the type of MRC task (extractive-style v.s. multiple-response-items-style), candidate answer
-A is not required in the sequence of tokens for the extractive-style MRC task.
-4Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-3.2 H IERARCHICAL KNOWLEDGE ENHANCEMENT MODULE
-This module is the implementation of our proposed HMP mechanism to fuse information of textual
-and graph context. We will formally introduce graph construction for UKET, and the three sub-
-processes of HMP in detail in the following sections.
-3.2.1 C ONSTRUCTION OF UKET G RAPH
-(1) Given a set with jQjelements:fGq
-k(Eq
-k;Rq
-k)gjQj
-q=1and input text, where jQjis the total number of
-injected KGs, and qindicates the q-th KG. We denote the set of entity mentions related to the q-th
-KG asXq=fxq
-igjXqj
-i=1, wherejXqjis the number of entity mentions in the text. The corresponding
-mentioned entities are shared by all tokens in the same entity mention. All mentioned entities
-Mq=fmq
-igjMqj
-i=1are linked with their relevant entity mentions in the text, where jMqjis the number
-of mentioned entities in the q-th KG. We define this "entity link to token graph" in Fig. 1 as
-Gq
-m(Eq
-m;Rq
-m), whereEq
-m=Xq[Mqis the union of entity mentions and their relevant mentioned
-entities,Rq
-mis a set with only one element that links mentioned entities and their relevant entity
-mentions. (2) For i-th mentioned entity mq
-iinMq, we retrieve all its K-hop neighbors fNx
-mq
-igK
-x=0
-from theq-th knowledge graph, where Nx
-mq
-iis a set ofi-th mentioned entity’s x-hop neighbors, hence
-we haveN0
-mq
-i=fmq
-ig. We define "KG-only graph": Gq
-s(Eq
-s;Rq
-s), whereEq
-s=SjMqj
-i=0SK
-x=0Nx
-mq
-iis the
-union of all mentioned entities and their neighbors within the K-hops sub-graph, and Rq
-sis a set
-of all relations in the extracted sub-graph of q-th KG. (3) The text sequence can be considered as
-a fully-connected word graph as pointed out previously. This “text-only graph” can be denoted as
-Gt(Et;Rt), whereEtis all tokens in text and Rtis a set with only one element that connects all
-tokens. Finally, we define the full hierarchical graph consisting of all three parts fGq
-sgjQj
-q=1,fGq
-mgjQj
-q=1,
-andGt, as Unified Knowledge-enhanced Text Graph (UKET).
-3.2.2 D YNAMICALLY EMBEDDING KNOWLEDGE CONTEXT
-We use pre-trained vectors obtained from the KGE method to initialize representations of entities in
-Gq
-s(Eq
-s;Rq
-s). Considering the structural information of injected knowledge graph forgotten during
-training, we utilize jQjindependent GNN encoders (i.e. g1(:),g2(:)in Fig. 2, which is the case of
-injecting two independent KGs in our experiment setting) to dynamically update entity embeddings
-ofjQjinjected KGs. We use rGCN to model the multi-relational nature of the knowledge graph. To
-updatei-th node ofq-th KG inl-th rGCN layer:
-~ sq(l+1)
-i =(X
-r2Rq
-sX
-j2Nr
-i1
-jNr
-ijWq(l)
-r~ sq(l)
-j)(1)
-WhereNr
-iis a set of neighbors of i-th node under relation r2Rq
-s.Wq(l)
-ris trainable weight matrix
-atl-th layer and ~ sq(l+1)
-i is the hidden state of i-th node at ( l+1)-th layer. After Lupdates,jQjsets
-of node embeddings are obtained. The output of the q-th KG can be represented as Sq2RjEq
-sjdq,
-wherejEq
-sjanddqare the numbers of nodes of extracted sub-graph and the dimension of pre-trained
-KGE, respectively.
-3.2.3 D YNAMICALLY SELECTING SEMANTICS -RELATED MENTIONED ENTITIES
-To handle the knowledge ambiguity issue, we introduce an attention layer to weight these ambiguous
-mentioned entities by using the textual representations of tokens (outputs of Section 3.1) to query
-their semantics-related mentioned entities representations in KGs. Here, we follow the attention
-mechanism of GAT to update each entity mention embedding in Gq
-m:
-~ xq
-i=(X
-j2Nq
-iq
-ijWq~ sq
-j)(2)
-Where~ sq
-jis the output embeddings from the q-th rGCN in the previous step. ~ xq
-iis the hidden state
-ofi-th entity mention xq
-iinXq, andNq
-iis a set of neighbors of xq
-iinGq
-m.Wq2Rdoutdinis a
-5Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-trainable weight matrix, we set din=dout=dq(thus~ xq
-i2Rdq).is a nonlinear activation function.
-q
-ijis the attention score that weights ambiguous mentioned entities in the q-th KG:
-q
-ij=exp(LeakyReLU (~ T
-q[Wq~ht0
-ijjWq~ sq
-j]))
-P
-k2Nq
-iexp(LeakyReLU (~ Tq[Wq~ht0
-ijjWq~ sq
-k]))(3)
-The representation ~ht
-iwith a dimension of dtis projected to the dimension of dq, before using it
-to query the related mentioned entity embeddings of Sq:~ht0
-i=Wq
-proj~ht
-i, whereWq
-proj2Rdqdt.
-~ q2R2dqis a trainable weight vector. 
Tis the transposition operation and jjis the concatenation
-operation.
-Finally, we concatenate outputs of jQjKGs with textual context representation to get final knowledge-
-enriched representation:
-~hk
-i= [~ht
-i;~ x1
-i;:::;~ xjQj
-i]2Rdt+d1+


+djQj (4)
-If tokentican’t match any entity in q-th KG (say ti=2Xq), we fill~ xq
-iin Eq.4 with zeros. Note
-that mentioned entities in KGs are not always useful, to prevent noise, we follow (Yang & Mitchell,
-2017)’s work and add an extra sentinel node linked to each entity mention in Gq
-m. The sentinel node
-is initialized by zeros and not trainable, which is the same as the case of no retrieved entities in the
-KG. In this way, according to the textual context, KELM can dynamically select mentioned entities
-and avoid introducing knowledge noise.
-3.2.4 I NTERACTION BETWEEN KNOWLEDGE -ENRICHED TOKEN EMBEDDINGS
-To allow knowledge-enriched tokens’ representations to propagate to each other in the text, we
-use a fully-connected word graph Gt, with knowledge-enriched representations from outputs of
-the previous step, and employ the self-attention mechanism similar to KT-NET to update token’s
-embedding. The final representation for i-th token in the text is ~hf
-i2R6(dt+d1+


+djQj).
-3.3 O UTPUT MODULE
-3.3.1 E XTRACTIVE -STYLE MRC TASK
-A simple linear transformation layer and softmax operation are used to predict start and end positions
-of answers. For i-th token, the probabilities to be the start and end position of answer span are:
-ps
-i=exp(wT
-s~hf
-i)
-nP
-j=1exp(wTs~hf
-j);pe
-i=exp(wT
-e~hf
-i)
-nP
-j=1exp(wTe~hf
-j), wherews;we2R6(dt+d1+


+djQj)are trainable vectors
-andnis the number of tokens. The training loss is calculated by the log-likelihood of the true start
-and end positions: L=1
-NNP
-i=1(logps
-ys
-i+logpe
-ye
-i), whereNis the total number of examples in the
-dataset,ys
-iandye
-iare the true start and end positions of i-th query’s answer, respectively. During
-inference, we pick the span (a;b)with maximum ps
-ape
-bwhereabas predicted anwser.
-3.3.2 M ULTIPLE -RESPONSE -ITEMS -STYLE MRC TASK
-Since answers to a given question are independent of each other, to predict the correct probability
-of each answer, a fully connected layer followed by a sigmoid function is applied on the final
-representation of [CLS]token in BERT.
-4 E XPERIMENTS
-4.1 D ATASETS
-In this paper, we empirically evaluate KELM on both two types of MRC benchmarks in Super-
-GLUE (Wang et al., 2020a): ReCoRD (Zhang et al., 2018) (extractive-style) and MultiRC (Khashabi
-6Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-et al., 2018) (multiple-response-items-style). Detailed descriptions of the two datasets can be found
-in Appendix B. On both datasets, the test set is not public, one has to submit the predicted results to
-the organization to get the final test score. Since frequent submissions to probe the unseen test set are
-not encouraged, we only submit our best model once for each of the datasets, thus the statistics of the
-results (e.g., mean, variance, etc.) are not applicable. We use Exact Match (EM) and (macro-averaged)
-F1 as the evaluation metrics.
-External Knowledge We adopt knowledge sources the same as used in KT-NET: WordNet and
-NELL (Carlson et al., 2010). Representations of injected knowledge are initialized by resources
-provided by (Yang & Mitchell, 2017). The size of these embeddings is 100. We retrieve related
-knowledge from the two KGs in a given sentence and construct UKET graph (as shown in Section
-3.2.1). More details about entity embedding and concepts retrieval are available in Appendix B.
-4.2 E XPERIMENTAL SETUPS
-Baselines and Comparison Setting Because we use BERT largeas the base model in our method,
-we use it as our primary baseline for all tasks. For fair comparison, we mainly compare our results
-with two fine-tune-based knowledge-enhanced models: KT-NET and SKG, which also evaluate
-their results on ReCoRD with BERT largeas the encoder part. As mentioned in the original paper
-of KT-NET, KT-NET mainly focuses on the extractive-style MRC task. We also evaluate KT-NET
-on the multiple-response-items-style MRC task and compare the results with KELM. We evaluate
-our approach in three different KB settings: KELM WordNet ,KELM NELL, and KELM Both, to inject KG
-from WordNet, NELL, and both of the two, respectively (The same as KT-NET). Implementation
-details of our model are presented in Appendix C.
-Dev Test
-Model EM F1 EM F1
-BERT large 70.2 72.2 71.3 72.0
-SKG+BERT large 70.9 71.6 72.2 72.8
-KT-NET WordNet 70.6 72.8 - -
-KT-NET NELL 70.5 72.5 - -
-KT-NET BOTH 71.6 73.6 73.0 74.8
-KELM WordNet 75.4 75.9 75.9 76.5
-KELM NELL 74.8 75.3 75.9 76.3
-KELM Both 75.1 75.6 76.2 76.7
-Table 1: Result on ReCoRD.Dev Test
-Model EM F1 EM F1
-BERT large - - 24.1 70.0
-KT-NET
-BOTH 26.7 71.7 25.4 71.1
-KELM WordNet 29.2 70.6 25.9 69.2
-KELM NELL 27.3 70.4 26.5 70.6
-KELM Both 30.3 71.0 27.2 70.8
-Table 2: Result on MultiRC. [*] are from our
-implementation.
-4.3 R ESULTS
-The results for the extractive-style MRC task and multiple-response-items-style MRC task are given in
-Table 1 and Table 2, respectively. The scores of other models are taken directly from the leaderboard
-of SuperGLUE6and literature (Qiu et al., 2019; Yang et al., 2019). In this paper, our implementation
-is based on a single model, and hence comparing with ensemble based models is not considered. Best
-results are labeled in bold and the second best are underlined.
-Results on the dev set of ReCoRD show that: (1) KELM outperforms BERT large, irrespective of
-which external KG is used. Our best KELM offers a 5.2/3.7 improvement in EM/F1 over BERT large.
-(2) KELM outperforms previous SOTA knowledge-enhanced PLM (KT-NET) by +3.8 EM/+2.3 F1 .
-In addition, KELM outperforms KT-NET significantly in all three KB settings. On the dev set of
-MultiRC, the best KELM offers a 3.6improvement in EM over KT-NET. Although the performance
-on F1 drop a little compared with KT-NET, we still get a gain of +2.9 (EM+F1) over the former
-SOTA model7.
-Results on the test set further demonstrate the effectiveness of KELM and its superiority over the
-previous works. On ReCoRD, it significantly outperforms the former SOTA knowledge-enhanced
-PLM (finetuning based model) by +3.2 EM/+1.9 F1 . And on MultiRC, KELM offers a 3.1/0.8
-improvement in EM/F1 over BERT large, and achieves a gain of +1.5 (EM+F1) over KT-NET.
-6https://super.gluebenchmark.com/leaderboard (Nov.14th, 2021)
-7The best model is chosen according to the EM+F1 score (same as KT-NET).
-7Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-5 C ASE STUDY
-This section uses an example in ReCoRD to show how KELM avoids knowledge ambiguity issue
-and selects the most relevant mentioned entities adaptively w.r.t the textual context. Recall that given
-a tokenti, the importance of a mentioned entity mq
-jinq-th KG is scored by the attention weight
-q
-ijin Eq.2. To illustrate how KELM can select the most relevant mentioned entities, we analyze
-the example that was also used in the case study part of KT-NET. The question of this example is
-“Sudan remains a XXX-designated state sponsor of terror and is one of six countries subject to the
-Trump administration’s ban”, where the “XXX” is the answer that needs to be predicted. The case
-study in KT-NET shows the top 3 most relevant concepts from WordNet for the word “ban” are
-“forbidding.n.01”, “proscription.n.01”, and “ban.v.02”, with the weights of 0.861, 0.135, and 0.002,
-respectively. KT-NET treats all synsets of a word as candidate KG concepts, both “forbidding.n.01”
-and “ban.v.02” will be the related concepts of the word “ban” in the text. Although KT-NET can
-select relevant concepts and suppress the knowledge noise through its specially designed attention
-mechanism, we still observe two problems from the previous case study: (1) KT-NET cannot select
-the most relevant mentioned entities in KG that share the same string in the input text. (2) Lack
-of ability to judge the part of speech (POS) of the word (e.g. “ban.v.02” gets larger weights than
-“ban.n.04”).
-Word in text
-(prototype)The most relevant
-mentioned entity in
-WordNet (predicted)Golden mentioned entity
-ford ford.n.05 (0.56) ford.n.05
-pardon pardon.v.02 (0.86) pardon.v.02
-nixon nixon.n.01 (0.74) nixon.n.01
-lead lead.v.03 (0.73) lead.v.03
-outrage outrage.n.02 (0.62) outrage.n.02
-Table 3: Case study. Comparisons between the golden label
-with the most relevant mentioned entity in WordNet. The
-importance of selected mentioned entities is provided in the
-parenthesis.For KELM, by contrast, we focus on
-selecting the most relevant mentioned
-entities to solve the knowledge ambi-
-guity issue (based on the “entity link
-to token graph” part of UKET). For in-
-jecting WordNet, by allowing message
-passing on the extracted sub-graphs
-(“KG only” part of UKET), knowl-
-edge context can be dynamically em-
-bedded according to the textual con-
-text. Thus the neighbors’ information
-of mentioned entities in WordNet can
-be used to help the word in a text to
-correspond to a particular POS based on its context. The top 3 most relevant mentioned entities in
-WordNet for the word “ban” in the above example are “ban.n.04”, “ban.v.02”, and “ban.v.01”, with
-the weights of 0.715, 0.205, and 0.060, respectively.
-To vividly show the effectiveness of KELM, we analyze ambiguous words in the motivating example
-show in Fig. 1 (The example comes from ReCoRD):
-“President Ford then pardoned Richard Nixon, leading to a further firestorm of outrage. ”
-Table. 3 presents 5 words in the above passage. For each word, the most relevant mentioned entity in
-WordNet with the highest score is given. The golden mentioned entity for each word is labeled by
-us. Definitions of mentioned entities in WordNet that correspond to the word examples are listing in
-Table 5 of Appendix.
-6 C ONCLUSION
-In this paper, we have proposed KELM for MRC, which enhances PLM representations with
-structured knowledge from KGs based on the fine-tuning process. Via a unified knowledge-enhanced
-text graph, KELM can embed the injected knowledge dynamically, and select relevant mentioned
-entities in the input KGs. In the empirical analysis, KELM shows the effectiveness of fusing external
-knowledge into representations of PLM and demonstrates the ability to avoid knowledge ambiguity
-issue. Injecting emerging factual knowledge into PLM during finetuning without re-pretraining
-the whole model is quite important in the application of PLMs and is still barely investigated.
-Improvements achieved by KELM over vanilla baselines indicate a potential direction for future
-research.
-8Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-ACKNOWLEDGEMENTS
-The authors thank Ms. X. Lin for insightful comments on the manuscript. We also thank Dr. Y . Guo
-for helpful suggestions in parallel training settings. We also thank all the colleagues in AI Application
-Research Center (AARC) of Huawei Technologies for their supports.
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-Linguistics , pp. 2346–2357, Florence, Italy, July 2019. Association for Computational Linguistics.
-doi: 10.18653/v1/P19-1226. URL https://www.aclweb.org/anthology/P19-1226 .
-Bishan Yang and Tom Mitchell. Leveraging knowledge bases in LSTMs for improving machine
-reading. In Proceedings of the 55th Annual Meeting of the Association for Computational Lin-
-guistics (Volume 1: Long Papers) , pp. 1436–1446, Vancouver, Canada, July 2017. Association for
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-anthology/P17-1132 .
-Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and
-relations for learning and inference in knowledge bases, 2015.
-Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Ruslan Salakhutdinov, and Quoc V . Le.
-Xlnet: Generalized autoregressive pretraining for language understanding, 2020.
-Donghan Yu, Chenguang Zhu, Yiming Yang, and Michael Zeng. Jaket: Joint pre-training of
-knowledge graph and language understanding, 2020.
-Sheng Zhang, Xiaodong Liu, Jingjing Liu, Jianfeng Gao, Kevin Duh, and Benjamin Van Durme.
-Record: Bridging the gap between human and machine commonsense reading comprehension,
-2018.
-Zhengyan Zhang, Xu Han, Zhiyuan Liu, Xin Jiang, Maosong Sun, and Qun Liu. Ernie: Enhanced
-language representation with informative entities, 2019.
-ASUMMARY AND COMPARISON OF RECENT KNOWLEDGE -ENHANCED PLM S
-Table 4 shows a brief summary and comparison of recent knowledge-enhanced PLMs. Most of recent
-work concentrated on injecting external knowledge graphs during pre-training phase, which makes
-them inefficient in injecting external knowledge (e.g. LUKE takes about 1000 V100 GPU days to
-re-pretraining the RoBERTa based PLM model). Also, nearly all of them uses an additional entity
-linking tool to align the mentioned entities in the Wikidata to the entity mentions in the pre-trained
-corpus (English Wikipedia) uniquely. These methods never consider to resolve the knowledge
-ambiguity problem.
-B D ATATSET AND KNOWLEDGE GRAPH DETAILS
-ReCoRD (an acronym for the Reading Comprehension with Commonsense Reasoning Dataset) is a
-large-scale dataset for extractive-style MRC requiring commonsense reasoning. There are 100,730,
-10,000, and 10,000 examples in the training, development (dev), and test set, respectively. An example
-of the ReCoRD consists of three parts: passage, question, and answer. The passage is formed by
-the first few paragraphs of an article from CNN or Daily Mail, with named entities recognized and
-marked. The question is a sentence from the rest of the article, with a missing entity specified as the
-golden answer. The model needs to find the golden answer among the entities marked in the passage.
-Questions that can be easily answered by pattern matching are filtered out. By the design of the
-process of data collection, one can see that to answer the questions, external background knowledge
-and ability of reasoning are required.
-MultiRC (Multi-Sentence Reading Comprehension) is a multiple-response-items-style MRC dataset
-of short paragraphs and multi-sentence questions that can be answered from the content of the
-paragraph. Each example of MultiRC includes a question that associates with several choices for
-answer-options, and the number of correct answer-options is not pre-specified. The correct answer is
-not required to be a span in the text. The dataset consists of 10K questions ( 6k multiple-sentence
-questions), about 60% of this data make training/dev data. Paragraphs in the dataset have diverse
-provenance by being extracted from 7 different domains such as news, fiction, historical text etc., and
-hence are expected to be more complicated in their contents as compared to single-domain datasets.
-11Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-Model Downstream Task Used KGsNeed
-Pre-trainDynamically
-Embedding KG
-ContextInject external
-KG’s RepresentationsSupport
-Multi-relationalSupport
-Multi-hopHandle Knowledge
-Ambiguity IssueBase Model
-ERNIE (Zhang et al., 2019)Glue, Ent Typing
-Rel CLSWikidataYes
-(MLM, NSP,
-Ent Mask task)NoInject pretrained
-entity embeddings
-(TransE) explicitlyNo
-(only entity embedding)NoNo
-(anchored entity mention to
-the unique id of Wikidata)BERT base
-K-BERT (Liu et al., 2019a)Q&A, NER
-Sent CLSCN-DBpedia
-HowNet, MedicalKGOptional
-(MLM, NSP)No NoYes
-(treat relations as words)NoNo
-(designed ATT mechanism
-can solve KN issue)BERT base
-KnowBERT (Peters et al., 2019)Rel Extraction
-Ent TypingCrossWikis,
-WordNetYes
-(MLM, NSP,
-Ent Linking task)NoInject both pretrained
-entity embeddings (TuckER)
-and entity definition explicitlyNo
-(only entity embedding)NoYes
-(weighed entity embeddings
-shared the same text)BERT base
-WKLM (Xiong et al., 2019) Q&A, Ent Typing WikidataYes
-(MLM, Ent
-replacement task)No No No NoNo
-(anchored entity mention to
-the unique id of Wikidata)BERT base
-K-Adapter (Wang et al., 2020c)Q&A,
-Ent TypingWikidata
-Dependency ParsingYes
-(MLM,
-Rel predition task)No NoYes
-(Via Rel prediction task
-during pretraining)NoNo
-(anchored entity mention to
-the unique id of Wikidata)RoBERTa large
-KEPLER (Wang et al., 2020d)Ent Typing
-Glue, Rel CLS
-Link PredictionWikidataYes
-(MLM,
-Link predition task)YesInject embeddings of
-entity and relation’s
-description explicitlyYes
-(Via link prediction task
-during pretraining)NoNo
-(anchored entity mention to
-the unique id of Wikidata)RoBERTa base
-JAKET (Yu et al., 2020)Rel CLS, KGQA
-Ent CLSWikidataYes
-(MLM, Ent Mask task,
-Ent category prediction,
-Rel type prediction)YesInject embeddings of
-entity descriptionsYes
-(Via Rel type prediction
-during pretraining)YesNo
-(anchored entity mention to
-the unique id of Wikidata)RoBERTa base
-CoLAKE (Sun et al., 2020)Glue, Ent Typing
-Rel ExtractionWikidataYes
-(MLM, Ent Mask task,
-Rel type prediction)Yes NoYes
-(treat relations as words)NoNo
-(anchored entity mention to
-the unique id of Wikidata)RoBERTa base
-LUKE (Yamada et al., 2020)Ent Typing, Rel CLS
-NER, Q&AEnt from
-WikipediaYes
-(MLM, Ent Mask task)No No No NoNo
-(treat hyperlinks in Wikipedia
-as entity annotations)RoBERTa large
-CokeBERT (Su et al., 2020)Rel CLS
-Ent TypingWikidataYes
-(MLM, NSP,
-Ent Mask task)YesInject pretrained
-entity embeddings
-(TransE) explicitlyYes
-(Via S-GNN to encode KG
-context dynamically)YesNo
-(anchored entity mention to
-the unique id of Wikidata)RoBERTa large
-SKG (Qiu et al., 2019) MRC WordNet, ConceptNet No YesInject pretrained
-entity embeddings
-(BILINER) explicitlyYes
-(Via multi-relational
-GNN to encode KG
-context dynamically)Yes No BERT large
-KT-NET (Yang et al., 2019) MRC WordNet, NELL No NoInject pretrained
-entity embeddings
-(BILINER) explicitlyNo
-(only entity embedding)NoYes
-(dynamically selecting
-KG context)BERT large
-KELM MRC WordNet, NELL No YesInject pretrained
-entity embeddings
-(BILINER) explicitlyYes
-(Via multi-relational
-GNN to encode KG
-context dynamically)YesYes
-(dynamically selecting
-related mentioned entity)BERT large
-Table 4: A brief summary and comparison of recent knowledge-enhanced PLMs. The full names
-of some abbreviations are as follows. MLM : masked language model, NSP: next sentence pre-
-diction, Ent: entity, Rel: relation, CLS : classification, Sent: sentence, ATT : attention. Com-
-ments/descriptions of features are written in parentheses. Desired properties are written in bold .
-WordNet contains 151,442 triplets with 40,943 synsets459and 18 relations. We look up mentioned
-entities in the WordNet by string matching operation, and link all tokens in the same word to the
-retrieved mentioned entities (tokens are tokenized by Tokenizer of BERT). Then, we extract all 1-hop
-neighbors for each mentioned entity and construct sub-graphs. In this paper, our experiment results
-are based on the 1-hop case. However, our framework can be generalized to multi-hop easily, and we
-leave this for future work.
-NELL contains 180,107 entities and 258 concepts. We link entity mentions to the whole KG, and
-return associated concepts.
-C I MPLEMENTATION DETAILS
-Our implementation is based on HuggingFace (Wolf et al., 2020) and DGL (Wang et al., 2020b).
-For all three settings of KELM, parameters of the encoding layer of BERT largeare initialized with
-pre-trained model released by Google. Other trainable parameters in HMP are randomly initialized.
-The total number of trainable parameters of KELM is 340.4M (Roughly the same as BERT large, which
-has 340M parameters). Since including all neighbors around mentioned entities of WordNet is not
-efficient, for simplicity, we use top 3 most common relations in WordNet in our experiment (i.e.
-hyponym, hypernym, derivationally_related_form). For both datasets, we use a “two stage” fine-tune
-strategy to achieve our best performance, the FullTokenizer built in BERT is used to segment input
-words into wordpieces.
-For ReCoRD, the maximum length of answer during inference is set to 30, and the maximum length
-of question is set to 64. Questions longer than that are truncated. The maximum length of input
-sequenceT8is set to 384. Input sequences longer than that are segmented into chunks with a stride
-of 128. Fine-tuning our model on ReCoRD costs about 18 hours on 4 V100 GPU with a batch size of
-48. We freeze parameters of BERT and use Adam optimizer with a learning rate of 1e-3 to train our
-knowledge module in the first stage. The maximum number of training epochs of the first stage is
-10. The purpose of this is to provide a good weight initialization for our HMP. For the second stage,
-the pre-trained BERT parameters and our HMP part will be fine-tuned together. The max number of
-training epochs is chosen from f4;6;8g. The learning rate is set to be 2e-5 with a warmup over the
-8Refer to the PLM Encoding Module of Methodology Section.
-12Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-first6%of max steps, and linear decay until up to max epochs. For both two stages, early stopping is
-applied according to the best EM+F1 score on the dev set every 500 steps.
-For MultiRC, the maximum length of input sequence Tis set to 256. The summation of length of
-question (Q) and length of candidate answer ( A) is not limited. Paragraph ( P) is truncated to fit the
-maximum length of input sequence. Fine-tuning KELM on MultiRC needs about 12 hours on 4 V100
-GPU with a batch size of 48. For the first stage finetuning, learning rate is 1e-4 and the maximum
-number of training epochs is 10. For the second stage, the max number of training steps is chosen
-fromf10000;15000;20000g. The learning rate is set to be 2e-5 with a warmup over the first 10% of
-max steps.
-D S UPPLEMENTATION OF THE CASE STUDY SECTION
-We provide definitions of the top 3 most relevant mentioned entities in WordNet that correspond to the
-word examples mentioned in Case Study Section . Descriptions are obtained by using NLTK (Loper
-& Bird, 2002). By comprehending the motivating example in the case study section, we can see that
-KELM can correctly select the most relevant mentioned entities in the KG.
-Word in text
-(prototype)Mentioned entity
-in WordNetDefinition
-banban.n.04 (0.72) an official prohibition or edict against something
-ban.v.02 (0.21) prohibit especially by legal means or social pressure
-ban.v.01 (0.06) forbid the public distribution of ( a movie or a newspaper)
-fordford.n.05 (0.56)38th President of the United States;
-appointed vice president and succeeded
-Nixon when Nixon resigned (1913-)
-ford.n.07 (0.24) a shallow area in a stream that can be forded
-ford.v.01 (0.08) cross a river where it’s shallow
-pardonpardon.v.02 (0.86) a warrant granting release from punishment for an offense
-sentinel (0.10) -
-pardon.n.02 (0.04) grant a pardon to
-nixonnixon.n.01 (0.74)vice president under Eisenhower and 37th President
-of the United States; resigned after the Watergate
-scandal in 1974 (1913-1994)
-sentinel (0.26) -
-leadlead.v.03 (0.73) tend to or result in
-lead.n.03 (0.12) evidence pointing to a possible solution
-lead.v.04 (0.05) travel in front of; go in advance of others
-outrageoutrage.n.02 (0.62) a wantonly cruel act
-sentinel (0.38) -
-Table 5: Definitions of mentioned entities in WordNet corresponding to the word examples in the case
-study. The importance of mentioned entities is provided in the parenthesis. “sentinel” is meaningless,
-which is used to avoid knowledge noise.
-E E XPERIMENT ON COMMONSENSE CAUSAL REASONING TASK
-To further explore the generalization ability of KELM, we also evaluate our method on COPA (Roem-
-mele et al., 2011) (Choice of Plausible Alternatives), which is also a benchmark dataset in SuperGLUE
-and can be used for evaluating progress in open-domain commonsense causal reasoning. COPA
-consists of 1000 questions, split equally into development and test sets of 500 questions each. Each
-question is composed of a premise and two alternatives, where the task is to select the alternative
-that more plausibly has a causal relation with the premise. Similar to the previous two MRC tasks,
-the development set is publicly available, but the test set is hidden. One has to submit the predicted
-results for the test set to SuperGLUE to retrieve the final test score. Since the implementation of
-KELM is based on BERT large, we use it as our baseline for the comparison. The result of BERT large
-is directly taken from the leaderboard of SuperGLUE. Table 6 shows the experiment results. The
-injected KG is WordNet here, and we use accuracy as the evaluation metric.
-The huge improvement over the baseline in this task demonstrates that knowledge in WordNet is
-indeed helpful for BERT to improve the generalization ability to the out-of-domain downstream task.
-13Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-Model dev test
-BERT large - 70.6
-KELMBERTlarge
-WordNet76.1 78.0
-Table 6: Performance comparison on COPA. The effectiveness of injecting knowledge (WordNet) are
-shown.
-F KELM: A FRAMEWORK OF FINETUNE -BASED MODEL -AGNOSTIC
-KNOWLEDGE -ENHANCED PLM
-We implement KELM based on the RoBERTa large, which has a similar number of trainable parameters
-asBERT largebut uses nearly 10 times of training corpus than BERT large. Since the performances of
-RoBEATa on the leaderboard of SuperGLUE are based on ensembling, we also finetune RoBERTa large
-on ReCoRD to produce the results of a single model. Comparisons of the results can be found
-in Table 7, where you can also see an improvement there. However, that improvement is not
-as significant as we observed in BERT large. Reasons are two-fold: (1) Passages in ReCoRD are
-collected from articles in CNN/Daily Mail, while BERT is pre-trained on BookCorpus and English
-Wikipedia. RoBERTa not only uses the corpus that used in BERT (16 GB), but also an additional
-corpus collected from the CommonCrawl News dataset (76 GB). ReCoRD dataset is in-domain
-for RoBERTa but is out-of-domain for BERT. It seems that the improvements of KELM with
-injecting general KGs (e.g. WordNet) on the in-domain downstream tasks are not as large
-as the out-of-domain downstream tasks . A similar phenomenon can be also observed in the
-experiment of SQuAD 1.1 (Refer to Appendix E). (2) The same external knowledge (WordNet,
-NELL) can not help RoBERTa largetoo much, since RoBERTa is pre-trained on a much larger corpus
-than BERT, knowledge in WordNet/NELL has been learned in RoBERTa.
-Dev Test
-Model EM F1 EM F1
-PLM w/o
-external knowledgeBERT large 70.2 72.2 71.3 72.0
-RoBERTa
-large 87.9 88.4 88.4 88.9
-knowledge enhanced PLM
-(finetune-based)KELMBERTlarge
-Both75.1 75.6 76.2 76.7
-KELMRoBERTalarge
-Both88.2 88.7 89.1 89.6
-knowledge enhanced PLM
-(pretrain-based)LUKE 90.8 91.4 90.6 91.2
-Table 7: Comparison of the effectiveness of injecting external knowledge between BERT and
-RoBERTa. [*] Results are from our implementation.
-We also list the results of LUKE (Yamada et al., 2020) in Table 7. LUKE is a pretrain-based
-knowledge enhanced PLM and uses Wiki-related golden entities (one-to-one mapping) as the injected
-knowledge source (about 500k entities9). It has more 128 M parameters than the total number
-of parameters of the vanilla RoBERTa. As we summarized in Table 4 in the main text, the pre-
-training task is also different compared with RoBERTa. Although LUKE gets better results compared
-with vanilla RoBERTa and KELM, it needs 16 NVIDIA Tesla V100 GPUs and the training takes
-approximately 30 days. Relying on hyperlinks in Wikipedia as golden entity annotations, lacking
-the flexibility to adapt the external knowledge of other domains, and needing re-pretraining when
-incorporating knowledge, these limitations hinder the abilities of applications.
-G E XPERIMENT ON SQUAD 1.1
-SQuAD1.1 (Rajpurkar et al., 2016) is a well known extractive-style MRC dataset that consists of
-questions created by crowdworkers for Wikipedia articles . It contains 100,000+ question-answer
-pairs on 536 articles. We implement KELM based on the BERT large, and compare our results on the
-development set of SQuAD 1.1 with KT-NET (Best result of KT-NET is based on injecting WordNet
-only). Results are shown in Table 8
-9For KELM, we only use 40943 entities in WordNet and 258 concepts in NELL.
-14Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-Dev
-Model EM F1
-PLM w/o external knowledge BERT large 84.4 91.2
-knowledge enhanced PLM
-(finetune-based)KT-NETBERTlarge
-WordNet85.1 91.7
-KELMBERTlarge
-WordNet84.7 91.5
-Table 8: Performance comparison on the development set of SQuAD 1.1.
-Results on KELM show an improvement over vanilla BERT. Both BERT and RoBERTa use English
-Wikipedia as the corpus for pretraining. Since SQuAD is also created from Wikipedia, it is an
-in-domain downstream task for both BERT and RoBERTa (while ReCoRD dataset is in-domain for
-RoBERTa but is out-of-domain for BERT). This explains why RoBERTa achieves a much larger
-improvement over BERT on the result of ReCoRD ( 71:3!88:4in EM on test set) than the one
-on SQuAD 1.1 ( 84:1!88:9). The rest of the improvement is because RoBERTa uses 10 times of
-training corpus than BERT and different pre-training strategies they used.
-Interestingly, we find the performance of KELM on SQuAD 1.1 is sub-optimal compared with KT-
-NET. As we mentioned in the last paragraph of the Related Work Section , KT-NET treats all synsets
-of entity mentions within the WN18 as candidate KB concepts. Via a specially designed attention
-mechanism, KT-NET can directly use all 1-hop neighbors of the mentioned entities. Although this
-limits the ability of KT-NET to select the most relevant mentioned entities (as we discussed in Case
-Study Section ), information of these neighbors can be directly considered. Using neighbors of the
-mentioned entities indirectly via the HMP mechanism makes it possible for KELM to dynamically
-embed injected knowledge and to select semantics-related mentioned entities. However, SQuAD
-is an in-domain downstream task for BERT, the problem of ambiguous meanings of words can be
-alleviated by pretraining model on the in-domain corpus. Compared with KT-NET, a longer message
-passing path in KELM may lead to sub-optimal improvement on the in-domain task.
-H F URTHER DISCUSSIONS ABOUT THE NOVELTY W .R.TSKG/KT-NET
-UKET defined in KELM consists of three subgraphs in a hierarchical structure, each subgraph
-corresponds to one sub-process of our proposed HMP mechanism and solves one problem presented
-in the Hierarchical Knowledge Enhancement Module part of Methodology Section . SKG only
-uses GNN to dynamically encode the extracted KG which corresponds to the first part of UKET, it can
-not solve the knowledge ambiguity issue and forbids interactions among knowledge-enriched tokens.
-KT-NET defines a similar graph as the third part of UKET. However, the first and second subgraphs
-of UKET are absent. The second subgraph of UKET is independent of ideas of KT-NET and SKG,
-thus KELM is not a simple combination of these two methods. We are the first to unify text and
-KG into a graph and to propose this hierarchical message passing framework to incorporate two
-heterogeneous information. SKG/KT-NET can be interpreted as parts of the ablation study of
-components of KELM . The result of SKG is ablation with the component only related to the first
-subgraph of UKET. While KT-NET only contains the third subgraph with a modified knowledge
-integration module. KELM uses a dedicatedly designed HMP mechanism to let the information of
-farther neighbors to be considered. However, longer information passing path than KT-NET makes
-it less efficient. In our experiments, KELM takes more 30% training time than KT-NET on both
-ReCoRD and MultiRC.
-I L IMITATIONS AND FURTHER IMPROVEMENTS OF KELM
-Limitations for KELM are two-fold: (1) Meanings of mentioned entities in different KGs that share
-the same entity mentions in the text may conflict with each other. Although HMP can help to select
-the most relevant mentioned entities in a single KG, there’s no mechanism to guarantee the selections
-across different KGs; (2) Note the knowledge-enriched representation in Eq.3 is obtained by simple
-concatenation of the embeddings from different KGs. Too much knowledge incorporation may
-divert the sentence from its correct meaning (Knowledge noise issue). We expect these two potential
-improvements to be a promising avenue for future research.
-15Accepted at the ICLR 2022 Workshop on Deep Learning on Graphs for Natural Language Processing
-J F URTHER ANALYSIS AND DISCUSSION
-KELM incorporates knowledge in KGs into the representations in the last hidden layer of PLM
-(Refer to Methodology Section ). It is essentially a model-agnostic, KG-agnostic, and task-agnostic
-framework for enhancing language model representations with factual knowledge from KGs. It
-can be used to enhance any PLM, with any injected KGs, on any downstream task. Besides the
-two Q&A-related MRC tasks we mentioned in the main text, we also evaluate KELM on COPA
-and SQuAD 1.1 based on BERT large, results are presented in Appendix E and Appendix G, respec-
-tively. To demonstrate KELM is a model-agnostic framework, we also implement KELM based on
-RoBERTa largeand evaluate it on ReCoRD. The experiment is presented in Appendix F. Improvements
-achieved by KELM over all vanilla base PLM models indicate the effectiveness of injecting external
-knowledge.
-However, the improvements of KELM over RoBERTa on ReCoRD and BERT on SQuAD 1.1 are
-marginal compared with the ones on ReCoRD/MultiRC/COPA ( BERT largebased). The reason behind
-this is that pretraining model on in-domain unlabeled data could boost performance on downstream
-tasks. Passages in ReCoRD are collected from articles in CNN/Daily Mail, while BERT is pre-trained
-on BookCorpus and English Wikipedia. RoBERTa not only uses the corpus that used in BERT
-(16 GB), but also an additional corpus collected from the CommonCrawl News dataset (76 GB).
-ReCoRD is in-domain for RoBERTa but is out-of-domain for BERT. Similarly, SQuAD 1.1 is
-created from Wikipedia, it is an in-domain downstream task for both BERT and RoBERTa. This
-partially explains why RoBERTa achieves a much larger improvement over BERT on the result of
-ReCoRD ( 71:3!88:4in EM on test set) than the one on SQuAD 1.1 ( 84:1!88:9). A similar
-analysis can be also found in T5 (Raffel et al., 2020). From our empirical results, we can summarize
-that general KG (e.g. WordNet) can not help too much for the PLMs pretrained on in-domain data.
-But it can still improve the performance of the model when the downstream tasks are out-of-domain.
-Further detailed analysis can be found in our appendix.
-Finding a popular NLP task/dataset that is not related to the training corpus of modern PLMs is
-difficult. Pre-training on large-scale corpus is always good if we have unlimited computational
-resources and plenty of in-domain corpus. It has been evident that the simple finetuning of PLM is
-not sufficient for domain-specific applications. KELM can provide people another choice when they
-do not have such a large-scale in-domain corpus and want to incorporate incremental domain-related
-structural knowledge into the domain-specific applications.
-16

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-The State of the Art when using GPUs in Devising Image Generation Methods Using Deep Learning  Yasuko Kawahata1 1 Department of Mathematical Informatics Graduate School of Information Science and Technology, The University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan.  Abstruct  Deep learning is a technique for machine learning using multi-layer neural networks. It has been used for image synthesis and image recognition, but in recent years, it has also been used for various social detection and social labeling. In this analysis, we compared (1) the number of Iterations per minute between the GPU and CPU when using the VGG model and the NIN model, and (2) the number of Iterations per minute by the number of pixels when using the VGG model, using an image with 128 pixels. When the number of pixels was 64 or 128, the processing time was almost the same when using the GPU, but when the number of pixels was changed to 256, the number of iterations per minute decreased and the processing time increased by about three times. In this case study, since the number of pixels becomes core dumping when the number of pixels is 512 or more, we can consider that we should consider improvement in the vector calculation part. If we aim to achieve 8K highly saturated computer graphics using neural networks, we will need to consider an environment that allows computation even when the size of the image becomes even more highly saturated and massive, and parallel computation when performing image recognition and tuning.  1. About Deep Learning(2015~2016)  Deep learning is a technique for machine learning using multilayer neural networks. In the past, technologies such as fuzzy networks and neural networks have been used for image synthesis [1] and image recognition [2] that incorporate elements of opportunity learning, but in recent years, they have quickly become one of the technologies that have attracted attention. Due to the progress of learning algorithms and the availability of a large amount of input data, the performance of neural networks has improved dramatically compared to conventional neural networks. Many fields where the market is expected to expand in the future, such as automatic driving, IoT (Internet of Things), and robotics, have a close relationship with machine learning or artificial intelligence, and are expected to develop into future applications.  The general recognition seems to be caused by the acquisition of DeepMind by Google, and the appearance of Google, IBM, and Caffe. In the 2014s, simple character recognition and image recognition packages such as Theano and Pylearn2 appeared, but after 2015, more powerful image recognition and processing packages using CUDA from NIVIDIA started to appear. From Google, APIs such as Cloud Vision API [3], Tensoflow, MXnet, and H2O, as well as packages and libraries that can be implemented more easily using Python and R have also appeared. Chainer, which uses the same Python library as Tensorflow, has also been introduced. Unlike Caffe, Chainer and Tensorflow are relatively easy to integrate with CUDA libraries, but they are even faster because they can be integrated with Python's acceleration package Cyton. It is also highly versatile in that it can be used with cuDNN, a GPU library for deep learning developed by NIVIDIA. METAMIND has started to use it in the field of x-ray image diagnosis in medicine[4]. Furthermore, in Japan, UEI announced the DEEPstation DK-1, a personal computer dedicated to deep learning, and Alpaca has started distributing APIs for image recognition and tools for predicting financial data[5, 6]. In January 2016, Microsoft also distributed toolkits such as CNTK, and we can say that it is becoming more familiar. As for Microsoft, in a project called "Catapult," we have been conducting research on improving computational efficiency by attaching FPGAs to servers in data centers. In the research of Catapult, they are not only building an accelerator engine by attaching FPGAs to the CPUs of servers, but also challenging the structure of a two-dimensional torus network using FPGAs to handle the communication between nodes. In a report in February 2016, we succeeded in accelerating the image recognition flow (FPGA) in Google's Tensorflow by a factor of although the performance of FPGA is usually 1/5 of that of GPU, it can be compensated by scaling up.  In deep learning, a CNN (Convolutional Neural Network), which mimics a neural system, is used, and a huge amount of computation is required to evaluate and train the input. We aim to improve the performance by one order of magnitude or more, with a cost increase of less than 30% and a power increase of less than 10%, by using the abundant resources of FPGAs to perform these calculations. Microsoft is aiming to popularize deep learning by providing the FPGA accelerator as a software library for users to use in the future[7]. In the field of deep learning, it can be said that there is a lot of discussion on how to speed up the learning of a large number of images, how to compete with other models for more detailed image recognition, and how to improve the development environment.  2. Expression techniques using Deep Learning(2015~2016)  As described in the previous section, the idea of using multilayer neural networks such as neural networks itself has been around for a long time, and these techniques have been used not only in industrial applications but also in the field of artistic expression[9]. With the advocacy of Genda et al. (1980), a new technique for expressing graphics has emerged, in which mathematical models are devised based on graphics such as images to be generated, using phenomena and things that occur in society and nature as input [9-13]. In addition, representation techniques that generate new images by evolutionary computation reflecting neural network operations in real time have been used in situations such as the self-organizing CG generation by Kawaguchi (2001)[14]. In addition, the environment on GPUs is becoming more and more important in the generation of graphics that require large-scale operations. Since the dawn of computer graphics, Genda et al(1980). have proposed a number of techniques for computer animation, such as the production of animation using random scan displays that take advantage of machine power[13]. And new graphic expressions based on the application of deep learning techniques are expected to increase in the future[15]. GPU-based operations are also necessary in the field of image recognition, which has outstanding results in deep learning compared to conventional machine learning. Nowadays, optimized GPU libraries have appeared to speed up image recognition techniques for deep learning such as cuDNN [16]. In this report, we compare the results of CPU and GPU benchmarks to see the difference in graphics generation between CPU and GPU benchmarks, in order to explore the potential of deep learning in image recognition, image generation, and graphics representation. By conducting this test, we would like to consider the scale, data size and machine for graphic representation using deep learning and batch plotting of analysis results of large-scale data in the future.   3. Consider which package to Use Cases(2015~2016) As described in Section 1, there are many libraries, packages, and toolkits related to deep learning, but in this case, we used Chainer, which has been reported to be an effective benchmark when using GPUs among the packages available until 2015[17]. Under the environment of a 6-core Intel Core i7-5930K CPU @ 3.50GHz + NVIDIA Titan X + Ubuntu 14.04 x86_64, the following results were obtained, and we decided to use Chainer in this report. Model Alexnet[18] Overfeat[19] OxfordNet[20] Googlenet V1[21] Chainer 177(sec) 620(sec) 885(sec) 687(sec) TensorFlow 277(sec) 843(sec) 1510(sec) 1084(sec) Caffe (native) 324(sec) 823(sec) 1068(sec) 1935(sec) Table 1: Execution times (in seconds) for various deep learning models under 6-core Intel Core i7-5930K CPU @ 3.50GHz + NVIDIA Titan X + Ubuntu 14.04 x86_64. )[18-21].     4. About Chainer(2015~2016) Chainer is a library for implementing neural networks developed by Preferred Networks [17]. This package is characterized by its support for CUDA. It is also possible to implement various types of neural networks, such as convolutional and recurrent, using Python.  5. Points to consider when installing Chainer(2015~2016) When you install Chainer, you need to install some related libraries.  You also need to install CUDA and cuDNN. Note that you need to register an account for cuDNN at the CUDA official website, answer a questionnaire about your usage, and download the cuDNN for your environment [16]. In addition, since Chainer depends on the Python library, it is also important to have an environment where the pip command can be used as follows.  $ su $ yum install python-setuptools $ wget http://peak.telecommunity.com/dist/ez_setup.py $ /usr/local/bin/python ez_setup.py $ easy_install pip $ easy_install numpy $ easy_install six $ easy_install Mako $ easy_install scipy $ pip install scikit-learn lspci | grep -i nvidia  Table 2: Codes for configuration (excerpt)  Before using GPU and cuDNN in Chainer, you should check your graphics board with the above command. Also, make sure to run Devicequery before running the program. The following assumes that CUDA7.5 is installed. Note that we recommend you to install Chainer 1.5 or later. The reason is that Chainer1.5 or later has no dependency with Pycuda, and thus there is no difficulty in passing through cuDNN in the later versions.   CPATH=$CPATH:/usr/local/cuda-7.5/include PATH=$PATH:/usr/local/cuda-7.5/bin CUDA_ROOT=/usr/local/cuda-7.5 export LD_LIBRARY_PATH=/usr/local/cuda-7.5:$LD_LIBRARY_PATH which nvcc ./deviceQuery sudo sh -c " cd /root/NVIDIA_CUDA-7.5_Samples/1_Utilities/deviceQuery/; ./deviceQuery " CPATH=$CPATH:/usr/local/cuda-7.5/lib64 PATH=$PATH:/usr/local/cuda-7.5/lib64 CUDA_ROOT=/usr/local/cuda-7.5/lib64 export LD_LIBRARY_PATH=/usr/local/cuda-7.5/lib64:$LD_LIBRARY_PATH CPATH=$CPATH:/usr/local/cuda-7.5/bin PATH=$PATH:/usr/local/cuda-7.5/bin CUDA_ROOT=/usr/local/cuda-7.5/bin export LD_LIBRARY_PATH=/usr/local/cuda-7.5/bin:$LD_LIBRARY_PATH CPATH=$CPATH:/usr/local/cuda-7.5/include PATH=$PATH:/usr/local/cuda-7.5/include CUDA_ROOT=/usr/local/cuda-7.5/include export LD_LIBRARY_PATH=/usr/local/cuda-7.5/include:$LD_LIBRARY_PATH Table 3: Codes for setting up the system (excerpt)  6. Models Handled(2015~2016) In this report, we compare the computational results of the NIN model and the VGG model [22] based on A Neural Algorithm of Artistic Stlye proposed as a method for image generation in deep learning, and the benchmarks of CPU, GPU, and cuDNN. The basis of our model is the Convolutional Learning Model. The basis of our model is to generate an image using Convolutional Neural Network.
- Figure 1: Picture of the model flow. 
- The numbers in Figure 1 mean [number of channels * height * width]. The basic model is based on a Convolutional Neural Network, and is generated from parameters that are intermediate between the main features of the two images. Assume that the input images are two images of 128*128 pixels, and the number of channels is 3 since the input images are RGB tri-color. As the layers increase, the number of channels increases, and the intermediate values of line thickness and line thinness are acquired and redrawn for representation. In this figure, we used 256*256 as an example, but we found that the algorithm works even if the resolution is changed, and the processing time becomes longer as the size is larger. In this algorithm, the output from the intermediate layers 1-4 is used. 
- Figure 2: Channel flow  The following are the generated images.
- Figure 3: Generated image (VGG model) 
- In addition, the correlation between the three channels output by Stylenet is calculated, and the features of the entire image, as well as the thickness and fineness of the lines, are represented in each intermediate layer.  
- Figure 4: Flow of feature acquisition. By outputting these results, the computational results are reflected in the generated image. The idea of the overall objective function is to correlate the difference between the image to be synthesized and the image of the middle layer to be minimized, and the difference in color and line between the Stylenet and the image, and to make a vector. 
- Figure 5: The flow of obtaining image features.  In this way, the difference between the former and the latter style images can be measured in both shallow and deep layers, and information such as fine brush strokes can be extracted in the shallow layers, while larger spatial patterns can be extracted in the deep layers. 
-  
- Figure 6: The overall flow  As shown in Fig. 6, in the initial stage, the image was generated by gradually repeating the calculation, starting from a figure reflecting only RGB information, gradually reflecting the special quantity of lines, and obtaining the intermediate quantity of line depth.  7. Calculation Results The environment used in this report is Intel(R) Core(TM) i5-4460 CPU @ 3.20GHz + NVIDIA Corporation GM206 [GeForce GTX 960] + CentOS Linux release 7.2.1511 (Core). In this section, we compare (1) the number of Iterations per minute between the GPU and the CPU when the VGG model is used and when the NIN model is used, and (2) the number of Iterations per minute by the number of pixels when the VGG model is used, using an image with 128 pixels. Through (1) and (2), we discussed the number of calculations per minute on the GPU according to each model and the number of pixels handled. 
- Figure 7: Comparison of GPU and CPU when using VGG model and NIN model. Number of iterations per minute, using an image with 128 pixels. Time(minutes) VGG(CPU)/ITERATION(Per Count) VGG(GPU) /ITERATION (Per Count) NIN(CPU) /ITERATION (Per Count) NIN(GPU) /ITERATION (Per Count) 1(Per Minutes) 71.4 721.9 108.9 980 2 142.9 1443.7 217.8 1960 3 214.3 2165.6 326.7 2940 4 285.7 2887.4 435.6 3920 5 357.1 3609.3 544.4 4900 Table 4: Comparison of the number of Iterations per minute between the GPU and CPU when using the VGG model and when using the NIN model (Iterations up to 5 minutes, using an image with 128 pixels).   Using an image with 128 pixels, we compared the number of iterations per minute between the GPU and CPU when using the VGG model and when using the NIN model. In Figure 7 and Table 4, the comparison of VGG and NIN models by model and environment is omitted because the cuDNN processing is supposed to improve the speed, but it is faster when only GPU mode is used.   01000200030004000500060007000
-14710131619222528313437404346495255586164ITERATIONTime:ScoreIterations per minute for each modelIteration Count
-VGG(CPU)VGG(GPU)NIN(CPU)NIN(GPU)As can be seen from Figs. 2 and 3, VGG is a model that strictly reflects the accuracy of the generated image and the intermediate amount of pixels, so the computation was somewhat slower than NIN, with fewer iterations per minute even in GPU mode. The issue here is how to speed up the computation of VGG, which will be a theme in the future.
- Figure 8: Comparison by image size when using VGG model. (Number of Iterations per minute) Time (minutes) ITERATION times of VGG (CPU in case of 64 pixels) TERATION times of VGG (GPU in case of 64 pixels) ITERATION times of VGG (CPU in case of 128 pixels) ITERATION times of VGG (GPU for 128 pixels) ITERATION times of VGG (CPU in case of 256 2-pixel number) ITERATION of VGG (GPU for 256 pixels) 1 94.2 847.8 71.4 721.9 32.0 286.7 2 188.3 1695.5 142.9 1443.7 64.1 573.3 3 282.6 2543.3 214.3 2165.6 96.1 860.1 4 376.8 3391.0 285. 7 2887.4 128.1 1146.7 5 471.0 4238.8 357.1 3609.3 160.1 1433.4 Table 5: Comparison of Iterations per minute by pixel count when using VGG model (Iterations up to 5 minutes) 010002000300040005000600070001815222936435057647178859299106113120127134141148155162169176ITERATION時間:分数Comparison of image size when using VGG modelComparison by Image Size (Iteration Count per Minute)
-VGG(64:CPU)VGG(64:GPU)VGG(128:CPU)VGG(128:GPU)VGG(256:CPU)VGG(256:GPU)We also compared the number of Iterations per minute by the number of pixels when using the VGG model. In the above comparison of models and environments in the VGG model in Fig. 8 and Table 5, we omitted the cuDNN processing in this example because it should have improved the speed, but it was faster in the GPU mode only. This is an issue to be addressed in the future.  However, when the number of pixels was changed to 256, the number of Iteations per minute was reduced, and the processing time was increased by a factor of about three. As a future prospect, when we consider the case of generating highly saturated images with the size of 1920 pixels or more, we can consider that we should consider improvements in the vector calculation part, because in this case, the core dumping occurs when the number of pixels is 512 or more.  8. Prospect  In this report, we were able to generate images with clearer material detection by using GPU operations, but the problem is that the deep learning flow itself requires a large amount of computation time, and the image size of 512 pixels causes core dumping in the environment of this report. In the future, if we aim to achieve 8K highly saturated computer graphics using neural networks such as those of Genda(1990) and Kawaguchi(2000), it will be necessary to consider the construction of an environment in which computation is possible even when the size of the image becomes even more saturated and massive, and parallel computation when performing image recognition and tuning.In the future, we can expect to use GPUs for machine learning of big data using deep learning, and we would like to discuss image generation based on image learning in this report, as well as application examples.  Acknowledgments  This research is the Result of Research Project 15J06703(Japan Society for the Promotion of Science PD) "Mathematical empirical analysis of past complex social phenomena using historical materials". In addition, the Graduate School of Information Science and Engineering, the University of Tokyo, who provided the GPU analysis environment for this research. I would like to thank Professor NAKATA Toshiyuki of the Social ICT Research Center.  References  [1] Keita Nakamura, et al. "Construction of the underwater environment of an object based on the laws of physics." Proceedings of the Academic Lecture Meeting of the Japan Society for Precision Engineering 2008.0 (2008): 1001-1002. [2] Hitoshi Yatomi, and Masafumi Hagiwara. "General-purpose image recognition using adaptive fuzzy inference neural networks and active search methods (video / multimedia and pattern recognition / understanding)." Technical Report of the Society of Electronics, Information and Communication Engineers. IE, Image Engineering 103.206 (2003): 17-22.  [3]Cloud Vision(Ref:2016/01)  https://cloud.google.com/vision/ [4] $ 8 Million from Deep Learning Entrepreneur Marc Benioff et al.(2014) (Ref:2016/01)  http://thebridge.jp/2014/12/deep-learning-startup-metamind-launches-with-8m-from-benioff-khosla-ventures-pickupnews [5]Alpaca(Ref:2016/01)  http://www.alpaca.ai/ [6] Deep Station(Ref:2016/01) https://deepstation.jp/ [7]Microsoft News (Ref:2016/01)  http://research.microsoft.com/apps/catalog/default.aspx?t=news [8] Lane, Nicholas D., et al. "An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices." Proceedings of the 2015 International Workshop on Internet of Things towards Applications. ACM, 2015. [9] Tomohiro Ohira, Etsuo Genda. "Graphics as seen from the click of ITECa (Computer collection of the 1980 Spring Tournament)" Design Research 31 (1980): 26-27. [10] Takashi Yoneyama, Etsuo Genda, Kunio Kondo. "Vision is the parameter of painting." Painting Studies 43.4 (2009): 13-21. [11] Takeshi Shibamoto, Etsuo Genda. "Application of Laser Beam to Display (25th Research Laser Program Collection)" Design Study 28 (1978): 48-49. [12] Minoru Takayama, Etsuo Genda. "Shaping metaball" A shape expression that shapes a "metaball". JSSD Japanese Society for Design Research Competition 51.0 (2004): C04-C04. [13] Etsuo Genda. "Computer Animation Virtual Research (2): Creation of Random Scan Display Animation (29th Research Display Conference Online Collection)" Design Studies 39 (1982): 15-16. [14] Takahiro Harada et al. "Simulation of particle method simulation on the GPU of the scope" Doctor of Computational Engineering Proceedings 13.1 (2008): 289-290. [15] Yoichiro Kawaguchi. "Self-organizing CG art-new" Jemotion "mother (re-digital & art)." Collage 3 (2001): 15-19. [16] https://developer.nvidia.com/cudnn [17]Chainer(Ref:2015/12)  http://chainer.org/ [18]Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images." Computer Vision and Pattern Recognition (CVPR), 2015 IEEE Conference on. IEEE, 2015. [19] Sermanet, Pierre, et al. "Overfeat: Integrated recognition, localization and detection using convolutional networks." arXiv preprint arXiv:1312.6229 (2013). [20] Kiros, Ryan, Ruslan Salakhutdinov, and Richard S. Zemel. "Unifying visual-semantic embeddings with multimodal neural language models." arXiv preprint arXiv:1411.2539 (2014). [21] Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015. [22] VGG Convolutional Neural Networks Practical(Ref:2016/01) http://www.robots.ox.ac.uk/~vgg/practicals/cnn/ [23] Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013). [24]Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. "A neural algorithm of artistic style." arXiv preprint arXiv:1508.06576 (2015). [25]Belanger, David, and Andrew McCallum. "Structured Prediction Energy Networks." arXiv preprint arXiv:1511.06350 (2015). [26]Lin, Tsung-Yu, and Subhransu Maji. "Visualizing and Understanding Deep Texture Representations." arXiv preprint arXiv:1511.05197 (2015). 

txt/2110.10486.txt

@@ -1,1576 +0,0 @@
-1
-A TinyML Platform for On-Device Continual
-Learning with Quantized Latent Replays
-Leonardo Ravaglia, Manuele Rusci, Davide Nadalini, Alessandro Capotondi,
-Francesco Conti, Member, IEEE , Luca Benini, Fellow, IEEE
-Abstract —In the last few years, research and development on
-Deep Learning models & techniques for ultra-low-power devices
-– in a word, TinyML – has mainly focused on a train-then-
-deploy assumption, with static models that cannot be adapted to
-newly collected data without cloud-based data collection and fine-
-tuning. Latent Replay-based Continual Learning (CL) techniques
-[1] enable online, serverless adaptation in principle, but so far
-they have still been too computation- and memory-hungry for
-ultra-low-power TinyML devices, which are typically based on
-microcontrollers. In this work, we introduce a HW/SW platform
-for end-to-end CL based on a 10-core FP32 -enabled parallel
-ultra-low-power (PULP) processor. We rethink the baseline La-
-tent Replay CL algorithm, leveraging quantization of the frozen
-stage of the model and Latent Replays (LRs) to reduce their
-memory cost with minimal impact on accuracy. In particular,
-8-bit compression of the LR memory proves to be almost lossless
-(-0.26% with 3000LR) compared to the full-precision baseline
-implementation, but requires 4 less memory, while 7-bit can
-also be used with an additional minimal accuracy degradation
-(up to 5%). We also introduce optimized primitives for forward
-and backward propagation on the PULP processor, together
-with data tiling strategies to fully exploit its memory hierarchy,
-while maximizing efficiency. Our results show that by combining
-these techniques, continual learning can be achieved in practice
-using less than 64MB of memory – an amount compatible with
-embedding in TinyML devices. On an advanced 22nm prototype
-of our platform, called VEGA , the proposed solution performs on
-average 65faster than a low-power STM32 L4 microcontroller,
-being 37more energy efficient – enough for a lifetime of 535h
-when learning a new mini-batch of data once every minute.
-Index Terms —TinyML, Continual Learning, Deep Neural
-Networks, Parallel Ultra-Low-Power, Microcontrollers.
-I. I NTRODUCTION
-The internet-of-Things ecosystem is made possible by
-miniaturized and smart end-node devices, which can sense the
-surrounding environment and take decisions based on the in-
-formation inferred from sensor data. Because of their tiny form
-L. Ravaglia, M. Rusci, D. Nadalini, F. Conti, and L. Benini are
-with the Department of Electrical, Electronic and Information Engineering
-(DEI) of the University of Bologna, Viale del Risorgimento 2, 40136
-Bologna, Italy (e-mail: fleonardo.ravaglia2, manuele.rusci, d.nadalini, f.conti,
-[email protected]).
-A. Capotondi is with the Department of Physics, Informatics and Mathe-
-matics of the University of Modena and Reggio Emilia, Via Campi 213/A,
-41125 Modena, Italy (e-mail: [email protected]).
-L. Benini is also with the integrated Systems Laboratory (IIS) of
-ETH Z ¨urich, ETZ, Gloriastrasse 35, 8092 Z ¨urich, Switzerland (e-mail:
-[email protected]).
-This work was supported in part by the ECSEL Horizon 2020 project
-AI4DI (Artificial intelligence for Digital Industry, g.a. no. 826060); and by EU
-Horizon 2020 project BonsAPPs (g.a. no. 101015848). We also acknowledge
-CINECA for the availability of high-performance computing resources and
-support awarded under the ISCRA initiative through the NAS4NPC project.
-Manuscript received May 15, 2021.factor and the requirement for low cost and battery-operated
-nature, these smart networked devices are severely constrained
-in terms of memory capacity and maximum performance and
-use small Microcontroller Units (MCUs) as their main on-
-board computing device [2]. At the same time, there is an ever-
-growing interest in deploying more accurate and sophisticated
-data analytics pipelines, such as Deep Learning (DL) inference
-models, directly on IoT end-nodes. These competing needs
-have given rise in the last few years to a specific branch of
-machine learning (ML) and DL research called TinyML [3] –
-focused on shrinking and compressing top-accurate DL models
-with respect to the target device characteristics.
-The primary limitation of the current generation of TinyML
-hardware and software is that it is mostly focused on inference .
-The inference task can be strongly optimized by quantizing [4]
-or pruning [5] the trained model. Many vendors of AI-oriented
-system-on-chips (SoCs) provide deployment frameworks to
-automatically translate DL inference graphs into human-
-readable or machine code [6]. This train-then-deploy design
-process rigidly separates the learning phase from the runtime
-inference, resulting in a static intelligence model design flow,
-incapable of adapting to phenomena such as data distribution
-shift: a shift in the statistical properties of real incoming data
-vs the training set that often impacts applications, causing the
-smart sensors platform to be unreliable when deployed in the
-field [7].
-Even if the algorithms themselves are technically capable
-to learn and adapt to new incoming data, the update process
-can only be handled from a centralized service, running on
-the cloud or host servers [8]. In this regard, the original
-training dataset would have to be enriched with the newly
-collected dataset, and the model would have to be retrained
-from scratch on the enlarged dataset, adapting to the new
-data without forgetting the original information [8]. Such an
-adaptive mechanism belongs to the rehearsal category and
-requires the storage of the full training set, often amounting
-to gigabytes of data. Additionally, large amounts of data
-have to be collected in a centralized fashion by network
-communication, resulting in potential security and privacy
-concerns, as well as issues of radio power consumption and
-network reliability in non-urban areas.
-We argue that a robust and privacy-aware solution to these
-challenges is enabling future smart IoT end-nodes to Life-
-long Learning, also known as Continual Learning [9](CL):
-the capability to autonomously adapt to the ever-changing
-surrounding environment by learning continually (only) from
-incoming data without forgetting the original knowledge – aarXiv:2110.10486v1  [cs.LG]  20 Oct 20212
-phenomenon known as catastrophic forgetting [10]. Despite
-many approaches exists to learn from data [11], recently the
-focus has moved to improve the recognition accuracy of DL
-models because of their superior capabilities, accounting on
-new data belonging to known classes ( domain-incremental
-CL) or a new classes ( class-incremental CL ) [12], [13]. The
-CL techniques recently proposed are grouped in three cate-
-gories: architectural, regularization and memory (or rehearsal)
-strategies. The architectural approaches specialize a subset
-of parameters for every (new and old) task but require the
-task-ID information at inference time, indicating the nature of
-current task in a multi-head network, and therefore they are
-not suitable for class or domain incremental continual learning.
-Concerning these latter scenarios, memory-based approaches,
-which preserve samples from previous tasks for replaying,
-perform better than regularization techniques, which simply
-address catastrophic forgetting by imposing constraints on the
-network parameter update at low memory cost [13]–[15]. This
-finding was confirmed during the recent CL competition at
-CVPR2020 [16], where the best entry leveraged on rehearsal
-based strategies.
-The main drawback of memory-based CL approaches con-
-cerns the high memory overhead for the storage of previous
-samples: the memory requirement can potentially grows over
-time preventing the applicability of these methods at the tiny
-scale, e.g. [17]. To address this problem, Pellegrini et al. [1]
-have recently introduced Continual Learning based on Latent
-Replays (LRs). The idea behind this is to combine a few old
-data points taken from the original training set, but encoded
-intoa low-dimensional latent space to reduce the memory
-cost, with the new data for the incremental learning tasks.
-Hence, the previous knowledge is retained by means of Latent
-Replays samples, i.e. the intermediate feature maps of the DL
-model inference, selected so that they require less space with
-respect to the input data (up to 48 smaller compared to raw
-images [1]). This strategy also leads to reduced computational
-cost: the Latent intermediate layer splits the network in a
-frozen stage at the front and an adaptive stage at the back,
-and only layers in the latter need to be updated. So far, LR-
-based Continual Learning has been successfully prototyped
-on high-performance embedded devices such as smartphones,
-including a Snapdragon-845 CPU running Android OS in the
-power envelope of a few Watts1. On the contrary, in this
-work, we focus on IoT applications and TinyML devices, with
-100tighter power constraints and 1000 smaller memories
-available.
-In our preliminary work [18], we proposed the early design
-concept of a HW/SW platform for Continual Learning based
-on the Parallel Ultra Low Power (PULP) paradigm [19], and
-assessed the computational and memory costs to deploy Latent
-Replay-based CL algorithms.
-In this paper, we complete and extend that effort by in-
-troducing several novel contributions from the software stack,
-system integration and algorithm viewpoint. To the best of our
-knowledge, we present the first TinyML processing platform
-1https://hothardware.com/reviews/qualcomm-snapdragon-845-
-performance-benchmarksand framework capable of on-device CL, together with the
-design flow required to sustain learning tasks within a few
-tens of mW of power envelope ( >10lower than state-of-
-the-art solutions). The proposed platform is based on VEGA ,
-a recently introduced end-node System-on-Chip prototype
-fabricated in 22nm technology [20]. Unlike traditional low-
-power and flexible MCUs design, VEGA exploits explicit
-data parallelism, by featuring a multi-core SW programmable
-RISC-V cluster with shared Floating Point Units (FPUs), DSP-
-oriented ISA and optimized memory management to enable
-the learning paradigm on low-end IoT devices. Additionally,
-to gain minimum-cost on-device retention of Latent Replays
-and better enable deployment on an ultra-low-power platform,
-we extend the LR algorithm proposed by Pellegrini et al. [1]
-to work with a fully quantized frozen front-end and compress
-Latent Replays using quantization down to 7 bits, with a small
-accuracy drop (almost lossless for 8-bit) when compared to the
-single-precision floating-point datatype ( FP32 ) on the Core50
-CL classification benchmark.
-In summary, the contributions of this work are:
-1) We extend the LR algorithm to work with an 8-bit
-quantized and frozen front-end without impact on the
-CL process and to support LR compression with quan-
-tization, reducing up to 4.5 the memory needed for
-rehearsing. We call this extension Quantized Latent
-Replay-based Continual Learning orQLR-CL .
-2) We propose a set of CL primitives including forward
-and backward propagation of common layers such as
-convolution, depthwise convolution, and fully connected
-layers, fine-tuned for optimized execution on VEGA, a
-TinyML platform for Deep Learning based on PULP
-[19], fabricated in 22nm technology. We also introduce
-a tiling scheme to manage data movement for the CL
-primitives.
-3) We compare the performance of our CL primitives on
-VEGA with that on other devices that could in the future
-target on-chip at-edge learning, such as a state-of-the-art
-low-power STM32L4 microcontroller.
-Our results show that the Quantized Latent Replay based Con-
-tinual Learning lead to a minimal accuracy loss on the Core50
-dataset compared to the FP32 baseline, when compressing the
-Latent Replay memory by 4by means of 8-bit quantization.
-Compression to 7 bit can also be exploited but at the cost of
-a slightly lower accuracy, up to 5% wrt the baseline when
-retraining one of the intermediate layer. When testing the
-QLR-CL pipeline on the proposed VEGA platform, our CL
-primitives demonstrated to run up to 65faster with respect
-to the MCUs for TinyML that can be found currently on the
-market. Compared against edge devices with a power envelope
-of 4W our solution is about 6more energy-efficient, enough
-to operate 317h with a typical battery for embedded devices.
-The rest of the paper is organized as follows: Section II
-discusses related work in CL, inference and learning at the
-edge, and hardware architectures targeted at edge learning.
-Section III introduces the proposed methodology for Quan-
-tized Continual Learning. Section IV describes the HW/SW
-architecture of the proposed TinyML. Section V evaluates and3
-discusses experimental results. Section VI concludes the paper.
-II. R ELATED WORK
-In this section, we first review the recent memory-efficient
-Continual Learning approaches before discussing the main
-solutions and methods for the TinyML ecosystem, including
-the first attempts for on-device learning on embedded systems.
-A. Memory-efficient Continual Learning
-Differently from Transfer Learning [21], [22], which by
-design does not retain the knowledge of the primitive learned
-task when learning a new one, Continual Learning (CL) has
-recently emerged as a new technique to tackle the acquisition
-of new/extended capabilities without losing the original ones
-– a phenomenon known as catastrophic forgetting [12], [13].
-One of the main causes of this phenomenon is that the newly
-acquired set breaks one of the main assumptions underlying
-supervised learning – i.e., that training data are statistically
-independent and identically distributed (IID). Instead, CL deals
-with training data that is organized in non-IID learning events .
-Maltoni et al. in [26] sort the main CL techniques intothree
-groups: rehearsal , which includes a periodic replay of the past
-information; architectural , relying on a specialized architec-
-ture, layers, and activation functions to mitigate forgetting;
-andregularization -based, where the loss term is extended to
-encourage retaining memory of pre-learned tasks.
-Among these groups, rehearsal CL strategies have emerged
-as the most effective to deal with catastrophic forgetting, at the
-cost of an additional replay memory [1], [27], [28]. In the re-
-cent CL challenge at CVPR2020 on the Core50 image dataset,
-90% of the competitors used rehearsal strategies [16]. The
-best entry of the more challenging New Instances and Classes
-track (the same scenario considered in our work) [17], which
-is evaluated in terms of test accuracy but also memory and
-computation requirements, scores 91% by replaying image
-data. Unfortunately, this strategy results untractable for an
-IoT platform because of the expanding replay memory (up
-to 78k images) and the usage of a large DenseNet-161 model.
-Conversely, the Latent Replay-based approach [1] relies on
-a fixed, and relatively small, amount of compressed latent
-activations as replay data; it scores 71% if retraining only the
-last layer, which presents a peak of 52lower (compressed)
-data points than the winning solution. Additionally, the Jodelet
-entry – also employing LR-based CL – achieves 83% thanks
-to 3more replays and a more accurate pre-trained model
-(ResNet50) [16]. In our work, we focus on [1] because of the
-tunable accuracy-memory setting. Nevertheless, our proposed
-platform and compression methodology can be applied to any
-replay-based CL approach.
-Also related to our work, ExStream [29] clusters in a
-streaming fashion the training samples before pushing them
-into the replay buffer while [30] uses discrete autoencoders
-to compress the input data for rehearsing. In contrast, we
-propose low-bitwidth quantization to compress the Latent
-Replay memory by >4and, at the same time, reduce the
-inference latency and the memory requirement of the inference
-task of the frozen stage if compared to a full-precision FP32
-implementation.B. Deep Learning at the Extreme Edge
-Two main trends can be identified for TinyML platforms
-targeting the extreme edge. On the one hand, Deep Learning
-applications are dominated by linear algebra which is an
-ideal target for application-specific HW acceleration [31], [32].
-Most efforts in this direction employ a variety of inference-
-only acceleration techniques such as pruning [33] and byte
-and sub-byte integer quantization [4]; the use of large arrays
-of simple MAC units [34] or even mixed-signal techniques
-such as in-memory computing [35].
-On the other hand, there are also many reasons for the
-alternative approach: running TinyML applications as soft-
-ware on top of commercial off-the-shelf (COTS) extreme-
-edge platforms, such as MCUs. Extreme-edge TinyML de-
-vices need to be very cheap; they have to be flexible due
-both to economy of scale and to their need for integration
-within larger applications, composed of both neural and non-
-neural tasks [36]. For these reasons, there is a strong push
-towards squeezing the maximal performance out of platforms
-based on COTS ARM Cortex-M class microcontrollers and
-DSPs, such as STMicroelectronics STM32 microcontrollers2,
-or on multi-core parallel ultra-low-power (PULP) end-nodes,
-like GreenWaves Technologies GAP-83. To cope with the
-severe constraints in terms of memory and maximum compute
-throughput of these platforms, a large number of deploy-
-ment tools have been recently proposed. Examples of this
-trend include non-vendor-locked tools such as Google TFLite
-Micro [6], ARM CMSIS-NN [37], Apache TVM [38], as
-well as frameworks that only support specific families of de-
-vices, such as STMicroelectronics X-CUBE-AI4, GreenWaves
-Technologies NNTOOL5, and DORY [39]. Internally, these
-tools employ hardware-independent techniques, such as post-
-training compression & quantization [40]–[42], as well as
-hardware-dependent ones such as data tiling [43] and loop
-unrolling to boost data reuse exploitation [37], coupled with
-automated generation of optimized backend code [44].
-As previously discussed, all of these efforts are mostly
-targeted at extreme edge inference, with little hardware and/or
-software dedicated to training. Most of the techniques used to
-boost inference efficiency are not as effective for learning.
-For example, the vast majority of training is done in full
-precision floating-point ( FP32 ) or, with some restrictions,
-using half-precision floats ( FP16 ) [45] – whereas inference is
-commonly pushed to INT8 or even below [4], [40]. IBM has
-recently proposed a specialized 8-bit format for training called
-HFP8 [46], but its effectiveness is still under investigation.
-Hardware-accelerated on-device learning has so far been
-limited to high-performance embedded platforms (e.g.,
-NVIDIA TensorCores on Tegra Xavier6and mobile platforms
-such as Qualcomm Snapdragon 845 [1]) or very narrow in
-scope. For example, Shin et al. [47] claim to implement an
-online adaptable architecture, but this is done using a simple
-2https://www.st.com/content/st com/en/ecosystems/stm32-ann.html
-3https://greenwaves-technologies.com/gap8 gap9/
-4https://www.st.com/en/embedded-software/x-cube-ai.html
-5https://greenwaves-technologies.com/sdk-manuals/nn quick start guide
-6https://www.nvidia.com/en-us/autonomous-machines/embedded-
-systems/jetson-xavier-nx4
-TABLE I
-ON-DEVICE LEARNING METHODS ON TINY EMBEDDED SYSTEMS .
-Method Learning Problem Proc. Tiny On-Device Compute Memory Continual
-Approach Device Device Learning Cost Cost Learning
-Transfer Retraining last Image Coral X LOW LOW
-Learning [21] layer’s weights Classification Edge TPU
-TinyTL Retraining Biases Image EPYC AMD X MEDIUM LOW /
-[22] Classification 7302 MEDIUM
-TinyOL Add layer for transfer-learning Anomaly Arduino Nano X X LOW LOW
-[23] based on streaming data Detection 33 BLE
-TinyML CNN backprop. from scracth Linear Camera GAP8 X - - X
-Minicar [8] on increasing dataset Class. 7 actions
-TML kNN Classifier Audio/Image STM32F7 X X LOW HIGH X
-[24] Class. 2 classes (unbounded)
-PULP-HD Hyperdimensional EMG 10 gestures Mr. Wolf X X MEDIUM LOW X
-[25] Computing Classification
-LR-CL CNN backprop. Image Qualcomm X HIGH HIGH / X
-[1] w/ LRs Class. 50 classes Snapdragon MEDIUM
-QLR-CL CNN backprop. Image VEGA X X HIGH MEDIUM X
-[This Work] w/ Quantized LRs Class. 50 classes
-LUT to selectively activate parameters, and does not support
-more powerful mechanisms based on gradient descent. A
-few recently proposed hardware accelerators for low-power
-training platforms [48]–[51] enable partial gradient back-
-propagation by using selective and compressed weight updates,
-but they do not address the large memory footprint required
-by training. Finally, several online-learning devices using bio-
-inspired algorithms such as Spiking Neural Networks [52] and
-High-Dimensional Computing [25] have been proposed [53]–
-[55]. Most of these approaches, however, have only been
-demonstrated on simple MNIST-like tasks.
-In this work, we propose the first, to the best of our
-knowledge, MCU-class hardware-software system capable of
-continual learning based on gradient back-propagation with
-a LR approach. We achieve these results by leveraging on
-few key ideas in the state-of-the-art: INT8 inference, FP32
-continual learning, and exploitation of linear algebra kernels,
-back-propagation, and aggressive parallelization by deploying
-them on a multi-core FPU-enhanced PULP cluster.
-C. On-Device Learning on low-end platforms
-Table I lists the main edge solutions featuring on-device
-learning capabilities. Every approach is evaluated by consid-
-ering the memory and computational costs for the continual
-learning task and the suitability for deployment on highly
-resource-constrained (tiny) devices.
-A first group of works deals with on-device transfer learn-
-ing. The Coral Edge TPU, which presents a power budget
-of several Watts, features SW support for on-device fine-
-tuning of the parameters of the last fully-connected layer [21].
-TinyTL [22] demonstrated on a high-end CPU that the transfer
-learning task results more effective (+32% on the target Image
-Classification task) by retraining the bias terms and adding lite
-residual modules. TinyOL [23] brought the transfer learning
-task on a tiny devices, i.e. an Arduino Nano platform featuring
-a 64MHz ARM Cortex-M4, by adding a trainable layer on top
-of a frozen inference model. Because only the coefficients of
-the last layer are updated during the online training process, nobackpropagation of error gradients applies. Compared to these
-works, we address a continual learning scenario and therefore
-we provide a more capable and optimized HW/SW solution
-to match the memory and computational requirements of the
-adopted CL method.
-Differently from the above works, de Prado et al. [8] pro-
-posed a Continual Learning framework for self-driving mini-
-cars. The embedded PULP-based MCU engine streams new
-data to a remote server, where the inference model is retrained
-from scratch on the enhanced dataset to improve the accu-
-racy over time. This fully-rehearsal methodology cannot be
-migrated to low-end devices because of the unconstrained in-
-crease of the memory footprint. In contrast, Disabato et al. [24]
-presented an online adaptive scheme based on a kNN classifier
-placed on top of a frozen feature extraction CNN model. The
-final stage is updated by incrementally adding the labeled
-samples to the knowledge memory of the kNN classifier.
-This approach has been evaluated on a tiny STM32F76ZI
-device but unfortunately has proven the effectiveness only
-on limited 2-classes problems and presents an unbounded
-memory requirement, which scales linearly with the number of
-training samples. PULP-HD [25] showed few-shot continual
-learning capabilities on an ultra-low power prototype using
-Hyperdimensional Computing. During the training phase the
-new data are mapped intoa limited hyperdimensional space
-by making use of a complex encoding procedure; at inference
-time the incoming samples are compared to the computed
-class prototypes. The method has been demonstrated on a
-10 gesture classification scenario based on EMG data but
-lacks of experimental evidences to be effective on complex
-image classification problems. In contrast to the these works,
-we demonstrate superior learning capabilities for a TinyML
-platform by i)running backpropagation on-device to update in-
-termediate layers, and ii)supporting a memory-efficient Latent
-Replay-based strategy to address catastrophic forgetting on a
-more complex Continual Learning scenario. An initial CNN-
-based prototype of a Continual Learning system was presented
-in in [1] using Latent Replays. The authors demonstrated the5
-Fig. 1. Continual Learning with Latent Replays. The frozen stage is the
-light-blue part (first half) of the network. After the first forward of the inputs
-(yellow arrow), activations (namely LRs) are stored apart. After having stored
-them, they will be used later mixed with the new images coming through the
-frozen stage and used to retrain the adaptive portion of the network.
-on-device learning capabilities using a Qualcomm Snapdragon
-processor, which features a power envelope 100 higher than
-our target and therefore it results not suitable for battery-
-operated tiny devices. In contrast to them, we also extend the
-LR algorithm by leveraging on quantization to compress the
-LR memory requirements.
-III. M ETHODS
-In this section, we analyze the memory requirements of the
-Latent Replay-based Continual Learning method and present
-QLR-CL , our strategy to reduce the memory footprint of the
-LR vectors based on a quantization process.
-A. Background: Continual Learning with Latent Replays
-In general, supervised learning aims at fitting an unknown
-function by using a set of known examples – the training
-dataset. In the case of Deep Neural Networks, the training
-procedure returns the values of the network parameters, such
-as weights and biases, that minimize a loss function. Among
-the used optimization strategies, the mini-batch Stochastic
-Gradient Descent (SGD), which is an iterative method applied
-over multiple learning step (i.e. the epochs), is widely adopted.
-In particular, The SGD algorithm computes the gradient of the
-parameters based on the loss function by back-propagating the
-error value through the network. This error function compares
-the model prediction, i.e. the output of the forward pass, with
-the expected outcome (the data label). Parameter gradients
-obtained after the backward pass are weighted over a mini-
-batch of data before updating the model coefficients.
-As introduced at the beginning of this work, the Latent
-Replay CL method [1] is a viable solution to gain TinyML
-adaptive systems with on-device learning capabilities based
-on the availability of new labeled data. In Fig. 1 we illustrate
-the CL process with Latent Replays. The new data are injected
-into the model to obtain the latent embeddings, which are the
-feature maps of a specific intermediate layer. We indicate such
-a layer with the index l, where l2[0; L), assuming the tar-
-geted model to be composed by Lstacked layers. At runtime,
-the new latent vectors are combined with the precomputed
-NLRLatent Replays vectors to execute the learning algorithm
-on the last Ll1layers. More specifically, the coefficientparameters of the adaptive stage are updated by using a mini-
-batch gradient descend algorithm. Every mini-batch includes
-both new data (in the latent embedding form) and LR vectors.
-The typical ratio of new data over the full mini-batch is 1/6 [1].
-The coefficient gradients are computed through forward and
-backward passes over the adaptive (learned) layers. Multiple
-iterations, i.e. the epochs, of the learning algorithms take place
-within the training procedure.
-B. Memory Requirements
-We model the Latent Replay-based Continual Learning task
-as operating on a set of new data coming from a sensor (e.g.,
-a camera), which is interfaced with an embedded digital pro-
-cessing engine, namely the TinyML Platform , and its memory
-subsystem. Given the limited memory capacity of IoT end-
-nodes, the quantification of the learning algorithm’s memory
-requirements is essential. We distinguish between two different
-memory requirements: additional memory necessary for CL,
-e.g., the LR memory, and that required to save intermediate
-tensors during forward-prop to be used for back-prop – a
-requirement common to all algorithms based on gradient
-descent, not specific to CL.
-Concerning the LR memory, the system has to save a
-set of NLRLRs, each one of the size of the feature map
-computed at the l-th layer of the network. In our scenario,
-LR vectors are represented employing floating-point ( FP32 )
-datatype and typically determine the majority of the memory
-requirement [18]. Since LRs are part of the static long-term
-memory of the CL system, for their storage, we use non-
-volatile memory, e.g., external Flash.
-On the other hand, forward- and back-prop of the network
-model require to allocate the space for NPnetwork parame-
-ters statically. In addition, forward-prop requires dynamically
-allocated buffers to store the activation feature maps for all
-layers. Up to the l-th layer, these buffers are temporary and
-can be released after their usage. Conversely, the system must
-keep in memory the feature maps after lto compute the
-gradients during back-prop. They can only be released after
-the corresponding layer has been back-propagated. Lastly, the
-system must also keep in memory the coefficients’ gradients,
-demanding a second array of NPelements. To keep accu-
-racy on the learning process, every tensor, i.e. coefficients,
-gradients, and activations, employ a FP32 format in our
-baseline scenario. Different from LRs, these tensors are kept
-intovolatile memories, except the frozen weights, which are
-stored in a non-volatile memory.
-C. Quantized Latent Replay-based Continual Learning
-Quantization techniques have been extensively used to re-
-duce the data size of model parameters, and activation feature
-maps for the inference task, i.e. the forward pass. An effective
-quantization strategy reduces the data bitwidth from 32-bit
-(FP32 ) to low bit-precision, 8-bit or less ( Qbits, in general)
-while paying an almost negligible accuracy loss.
-In this paper, we introduce the Quantized Latent Replay-
-based Continual Learning method (QLR-CL) relying on low-
-bitwidth quantization to speed up the execution of the network6
-SRAM
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-Logari thmi c InterconnectCore 0
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-Core 4
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-4x Shared Fl oating  Point Uni ts
-I$ I$ I$ I$ I$ I$ I$ I$Priv
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-SRAMTightly Coup led Data Mem ory 128 KBPrivate L2
-64 KBInterleaved L2
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-I/O DMASPI
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-MIPI
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-GPIO APB Bus Peripheral InterconnectINTC
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-Fig. 2. Architecture outline of the proposed PULP-based System-on-Chip for
-Continual Learning.
-up to the l-th layer and at the same time reduce the memory
-requirement of the LR vectors from the baseline FP32 arrays.
-To do so, we split the deep model intotwo sub-networks,
-namely the frozen stage and the adaptive stage . The frozen
-stage includes the lower layers of the network, up to the
-Latent Replay layer l. The coefficients of this sub-network,
-including batch normalization statistics, are frozen during the
-incremental learning process. On the contrary, the parameters
-of the adaptive stage are updated based on the new data
-samples.
-In QLR-CL, the Latent Replay vectors are generated by
-feeding the frozen stage sub-network with a random subset
-of training samples from the CL dataset, which we denote
-asXtrain. The frozen stage is initialized using pre-trained
-weights from a related problem – in the case of Core50,
-we use a network pre-trained on the ImageNet-1k dataset.
-Post-Training Quantization of the frozen stage is based on
-training samples Xtrain. We apply a standard Post-Training
-Quantization process that works by i)determining the dynamic
-range of coefficient and activation tensors, ii)dividing the
-range intoequal steps, using a uniform affine quantization
-scheme [56]. While the statistics of the parameters can be
-drawn without relying on data, the dynamic range of the acti-
-vation features maps is estimated using Xtrainas a calibration
-set. If we denote the dynamic range of the weights at the i-th
-layer of the network as [wi;min; wi;max], we can define the
-wi;quant INT-Q representation of parameters as
-wi;quant =wi
-Sw;i
-; S w;i=wi;maxwi;min
-2Q1(1)
-where Qis the number of bits, wiis the full-precision output
-of the frozen stage . The representation of activations is similar,
-but we further restrict (1) for activations aiby considering the
-effect of ReLU’s: aiare always positive and ai;quant can be
-represented using an unsigned UINT-Q format:
-ai;quant =ai
-Sa;i
-; S a;i=ai;max
-2Q1(2)
-where ai;max is obtained through calibration on Xtrain.
-Quantized Latent Replays (QLRs) al;replay are represented
-similarly to other quantized activations, setting the layer ito
-the LR l. Their value is initialized during the initial setup
-of the QLR-CL process using the latent quantized activations
-al;quant over the Xtrainset.During the QLR-CL process, the adaptive stage is fed by
-dequantized vectors obtained as Sa;l
al;replay , along with the
-dequantized latent representation of the new data sample Sa;l

-al;quant . Hence, the single FP32 parameter Sa;lis also stored
-in memory as part of the frozen stage . In our experiments, we
-set the bitwidth Qof all activations and coefficients to 8-bit,
-while the output of the frozen stage is compressed to 8-bit or
-less, as further explored in Section V.
-IV. H ARDWARE /SOFTWARE PLATFORM
-In this section, we describe the hardware architecture of
-the proposed platform for TinyML learning and the related
-software stack.
-A. Hardware architecture
-The CL platform we propose is inspired and extends on our
-previous work [18]. We build it upon an advanced PULP-based
-SoC, called VEGA , which combines parallel programming for
-high-performance with ultra-low-power features. An advanced
-prototype of this platform has been taped out in Global-
-Foundries 22nm technology [20]. The system architecture,
-which is outlined in Fig. 2, is based on an I/O-rich MCU
-platform coupled with a multi-core cluster of RISC-V ISA
-digital signal processing cores which are used to accelerate
-data-parallel machine learning & linear algebra code. The
-MCU side features a single RISC-V core, namely the Fabric-
-Controller (FC), and a large set of peripherals. Besides the
-FC core, the MCU-side of the platform includes a large L2
-SRAM, organized in an FC-private section of 64kB and a
-larger interleaved section of 1.5MB. The interleaved L2 is
-shared between the FC core and an autonomous I/O DMA
-controller, connected to a broad set of peripherals such as
-OctaSPI/HyperBus to access an external Flash or DRAM
-of up to 64MB, as well as camera interfaces (CPI, MIPI)
-and standard MCU interfaces (SPI, UART, I2C, I2S, and
-GPIO). The I/O DMA controller is connected to an on-chip
-magnetoresistive RAM (MRAM) of 4MB, which resides in its
-power and clock domain and can be accessed through the I/O
-DMA to move data to/from the L2 SRAM.
-The multi-core cluster features nine processing elements
-(PE) that share data on a 128kB multi-banked L1 tightly
-coupled data memory (TCDM) through a 1-cycle latency
-logarithmic interconnect. All cores are identical, using
-an in-order 4-stage architecture implementing the RISC-V
-RV32IMCFXpulpv2 ISA. The cluster includes a set of four
-highly flexible FPUs shared between all nine cores, capable
-ofFP32 andFP16 computation [57]. Eight cores are meant
-to execute primarily data-parallel code, and therefore they
-use a hierarchical Instruction cache (I$) with a small private
-part (512B) plus 4kB of shared I$ [58]. The ninth core is
-meant to be used as a cluster controller for control-heavy
-data tiling & marshaling operations; it has a private I$ of
-1kB. The cluster also features a multi-channel DMA engine
-that autonomously handles data transfers between the shared
-L1 and the external memories through a 64-bit AXI4 cluster
-bus. The DMA can transfer up to 8B/cycle between L2 and
-L1 TCDM in both directions simultaneously and perform 2D7
-FW
-BW error BW gradim2col transform K
-KCin
-K × K × C inK × K × C in
-1 × 1 × C out
-1 × 1 × C out
-output weight input
-K × K × C inK × K × C in 1 × 1 × C out
-1 × 1 × C out
-grad_w eight
-Linput grad_out put
-L1 × 1 × C out
-K × K × C in1 × 1 × C out
-K × K × C in
-weight grad_input grad_out put
-K × K × C in
-=.
-= = . .
-Fig. 3. Clockwise from top-left: im2col transform, Forward and Backward
-propagation for error and gradient calculation for a KKConv layer.
-Fig. 4. Tiling scheme between L2 and L1 memories exploiting double-
-buffering. Two equal buffers are filled with the matrix multiplication terms
-that fit intohalf the size of L1. The second buffer is filled with the next terms
-of the convolution that have to be matrix-multiplied.
-strided access on the L2 side by generating multiple AXI4
-bursts. The cluster can be switched on and off at runtime
-by the FC core employing clock-gating; it also resides on a
-separate power domain than the MCU, making it possible to
-completely turn it off and to tune its Vdd using an embedded
-DC-DC regulator.
-B. Software stack
-To execute the CL algorithm, the workload is largely
-dominated by the execution of convolutional layers, such as
-pointwise, and depthwise, or fully connected layers ( 98% of
-operations in MobileNet-V1). Consequently, the main load on
-computations is due to variants of matrix multiplications dur-
-ing the forward and backward steps, which can be efficiently
-parallelized on the 8 compute PEs of the cluster, leaving one
-core out to manage tiling and program data transfers. Thus,
-to enable the learning paradigm on the PULP platform, we
-propose a SW stack composed of parallel layer-wise primitives
-that realize the forward step and the back-propagation. The
-latter concerns either the computation of the activation gradi-
-ents ( backward error step ) and coefficient gradients ( backwardgradient step ). Fig. 3 depicts the dataflow of the forward and
-backward for commonly used convolutional kernels such as
-pointwise (PW), depthwise (DW), and linear (L) layers. To
-reshape all convolution operations intomatrix multiplications,
-theim2col transformation is applied to the activation tensors to
-reshape them into2D matrix operands [37]. The FP32 matrix
-multiplication kernel is parallelized over the eight cores of the
-cluster according to a data-parallelism strategy, making use of
-fmadd.s (floating multiply-add) instructions made available by
-the shared FPU engines.
-The cores must operate on data from arrays located in
-the low-latency L1 TCDM to maximize throughput and com-
-putational efficiency (i.e., IPC). However, the operands of a
-layer function may not entirely fit into the lower memory
-level because of the limited space (128kB). For instance, the
-tensors of the PW layer #22 of the used MobileNet-V1 occupy
-1.25MB. Hence, the operands have to be sliced intoreduced-
-size blocks that can fit intothe available L1 memory and
-convolutional functions are applied on L1 tensor slices to
-increase the computational efficiency.
-This approach is generally referred to as tiling [39], which
-is schematized in Fig. 4. By locating layer-wise data on the
-larger L2 memory (1.5MB), the DMA firstly copies individual
-slices of operand data, also referred to as tiles, intoL1 buffers,
-to be later fetched by the cores. Since the cluster DMA engine
-is capable of 2D-strided access on the L2 side, this operation
-can also be designed to perform im2col , without any manual
-data marshaling overhead on L1.
-To increase the computation efficiency, we implement a
-software flow that interleaves DMA transfers between L2 and
-L1 and calls to parallel primitives, e.g. forward ,backward
-error , orbackward gradient steps , which operate on individual
-tiles of data. Hence, every layer is expected to load and
-process all the tiles of any operand tensor. To reduce the
-overhead due to the data copy, the DMA transfers take place
-in the background of the multi-core computation: the copy
-of the next tile is launched before invoking the computation
-on loaded tiles. On the other side, this optimization requires
-doubling the L1 memory requirement: while one L1 buffer is
-used for computation, an equally-sized buffer is used by the
-data movement task. From a different viewpoint, the maximum
-tile size must not exceed half of the available memory. At
-runtime, layer-wise tiled kernels are invoked sequentially to
-run the learning algorithm with respect to the input data. To
-this aim, LRs are loaded from external embedded memory, if
-not fitting the internal memory, and copied to the on-chip L2
-memory thanks to the I/O DMA.
-V. E XPERIMENTAL RESULTS
-In this section, we provide the experimental evidence about
-our proposed TinyML platform for on-device Continual Learn-
-ing. First, we evaluate the impact of quantization of the frozen
-stage and the LR vectors upon the overall accuracy, and we
-analyze the memory-accuracy trade-off.
-Secondly, we study the efficiency of the proposed SW ar-
-chitecture with respect to multiple HW configurations, namely
-#cores, L1 size and DMA bandwidth, introducing the tiling8
-Fig. 5. Accuracy plots for NLR=f375;750;1500;3000gand different
-levels of quantization. From these plots it is visible that below UINT-7
-accuracy degrades rapidly.
-requirements and evaluating the latency for each kernel of
-computation. Then, we measure performance on an advanced
-PULP prototype, VEGA, fabricated in GlobalFoundries 22nm
-technology with 4 FPUs shared among all cores. We analyze
-the latency results for individual layers forward and backward
-and estimate the overall energy consumption to perform a CL
-task on our platform. Finally, we compare the efficiency of our
-TinyML platform to other devices used for on-device learning.
-A. Experimental Setup
-We benchmark the compression technique for the Latent
-Replay memory on the image-classification Core50 dataset,
-which includes 120k 128 128 RGB images of 50 objects
-for the training and about 40k images for the testing. On the
-Core50 dataset, the CL setting is regulated by the NICv2-
-391 protocol [59]. According to this protocol, 3000 images
-belonging to ten classes are made available during the initial
-phase to fine-tune the targeted deep model on the Core50
-problem. Afterward, the remaining 40 classes are introduced at
-training time in 390 learning events. Each event, as described
-more in detail in Section III-A, comprises iterations over mini-
-batches of 128 samples each: 21 coming from actual images,
-all from the same class and typically not independent (e.g.,
-coming from a video), and 107 latent replays. After each
-learning event, the accuracy is measured on the test set, which
-includes samples from the complete set of classes.
-Following [1], we use a MobileNet-V1 model with an
-input resolution of 128 128 and width multiplier 1, pre-
-trained on ImageNet; we start from their public released
-code7and use PyTorch 1.5. In our experiments, we replace
-BatchReNormalization with BatchNormalization layers and
-we freeze the statistics of the frozen stage after fine-tuning.
-B. QLR-CL memory usage and accuracy
-To evaluate the proposed QLR-CL setting, we quantize the
-frozen stage of the model using the PyTorch-based NEMO
-library [60] after fine-tuning the MobileNet-V1 model with
-7Available at https://github.com/vlomonaco/ar1-pytorch/. While Pelle-
-grini et al. [1] report lower accuracies in their paper, our FP32 baseline results
-are aligned with their released code.the initially available 3000 images. We set the activation and
-parameters bitwidth of the frozen stage to Q= 8bit while we
-vary the bitwidth QLRof the latent replay layer. The quantized
-frozen stage is used to generate a set of NLRLatent Replays,
-as sampled from the initial images.
-The plots in Fig. 5 show the test accuracy on the Core50
-that is achieved at the end of the NICv2-391 training protocol
-for a varying NLR=f375;750;1500;3000gwhile sweeping
-the LR layer l. Depending on the selected layer type, the size
-of the LR vector varies as reported in Table III.
-Each subplot of Fig. 5 compares the baseline FP32 ver-
-sion with our 8-bit fully-quantized solutions with a varying
-QLR=f8;7;6g, denoted in the figures, respectively, as UINT-
-8, UINT-7 and UINT-6. For a QLR<6, we observe the
-Continual Learning process to not converge on the Core50
-dataset.
-From the obtained results, we can observe the UINT-8
-compressed solution featuring a small accuracy drop with
-respect to the full-precision FP32 baseline. When increasing
-the number of latent replays NLRto 3000, the UINT-8
-quantized version results almost lossless (-0.26%), if LR = 19 .
-On the contrary, if the LR layer is moved towards the last
-layer (LR= 27), the accuracy drop increases up to 3.4%. The
-same effect is observed when reducing NLRto 1500, 750 or
-375. In particular, when NLR= 1500 , the UINT-8 quantatized
-version presents an accuracy drop from 1.2% (LR = 19 ) to
-2.9% (LR = 27 ). On the other hand, lowering the bit precision
-to UINT-7, the accuracy reduces on average of up to 5:2%, if
-compared to the FP32 baseline. Bringing this further down to
-UINT-6 largely degrades the accuracy by more than 10%.
-To deeply investigate the impact of the quantization process
-on the overall accuracy, we perform an ablation study to
-distinguish the individual effects of i)the quantization of
-the front-end and ii)the quantization of the LRs. In case
-ofNLR= 1500 , Table II compares the accuracy on the
-Core50 dataset for different LR layers, if applying quantization
-to both the LR memory and the frozen stage or only to
-the LR memory. The accuracy statistics are averaged over
-5 experiments; we report in the table the mean and the
-std deviation of the obtained results. In particular, we see
-that quantizing the LRs has a larger effect on the accuracy
-than quantizing the frozen graph. By quantizing only the LR
-memory to UINT-8, the accuracy drops by up to 1.2-2.6%
-(higher in case of larger adaptive stages) with respect to the
-FP32 baseline. On the contrary, the UINT-8 quantized frozen
-graph brings only an additional 0.5-1% of accuracy drop.
-With UINT-7 LRs, the accuracy drop is mainly due to the
-LR quantization: when compressing also the frozen stage to
-8-bit the accuracy drop is up to -1%, which is small compared
-to the total 4-7% of accuracy degradation.
-To facilitate the interpretation of the results, Fig. 6 reports
-the test accuracy for multiple quantization settings compared
-to the size (in MB) of the Latent Replay Memory. In red,
-we highlight a Pareto frontier of non-dominated points, to
-have a range of options to maximize accuracy and minimize
-the memory footprint. Among the best solutions, we detect
-two clusters of points on the frontier. The first cluster ( A),
-corresponding to the low-memory side of the frontier, is9
-TABLE II
-ACCURACY ON CORE50DATASET WITH MULTIPLE QUANTIZATION
-SETTINGS A+B ,WHERE ADENOTES THE QUANTIZATION OF THE FROZEN
-STAGE (FP32 ORUINT-8) AND BINDICATES THE QUANTIZATION SCHEME
-OF THE LR VECTORS (FP32, UINT-8, UINT-7). T HE BASELINE IS FP32.
-LR FP32 FP32 + UINT-8 + FP32 + UINT-8 +
-layer baseline UINT-8 UINT-8 UINT-7 UINT-7
-27 72.70.34 70.10.54 69.20.48 68.00.63 67.81.14
-25 73.30.58 70.90.65 70.20.67 66.20.75 66.10.94
-23 75.00.83 73.20.46 73.40.66 71.10.63 69.91.25
-21 76.50.63 74.90.51 73.91.67 72.70.74 72.61.30
-19 77.70.73 76.50.48 76.00.80 74.00.57 75.21.10
-TABLE III
-SIZE OF TILES FOR THE MOBILE NET-V1 LAYERS .
-LR Layer LR Dim. LR Size
-Layer l Type (HWC)(#elements)
-19 DW 88512 32k
-20 PW 88512 32k
-21 DW 88512 32k
-22 PW 88512 32k
-23 DW 44512 8k
-24 PW 441024 16k
-25 DW 441024 16k
-26 PW 441024 16k
-27 Linear 111024 1k
-constituted by experiments that use l= 27 with 1500 or 3000
-LRs and UINT-7 or UINT-8 representation. On the other hand,
-if we aim at the highest accuracy possible for our QLR-CL
-classification algorithm, we can follow the Pareto frontier to
-the right towards higher accuracies at steeper memory cost,
-reaching cluster B. All points in cluster Bfeatures l= 23
-as Latent Replay layer, which is a bottleneck layer of the
-network and allows to store more compact tensors as LR (refer
-to Table III). Adopting LR layers within Bleads accuracy to
-an average of 76%, gaining5%on average with respect to
-the layers within cluster A. A single point C1is shown further
-to the right, but still below 128MB.
-For a deeper analysis of the Pareto frontier, in Fig. 7, we
-detail the memory requirements when analyzing the points
-into the two clusters AandB, as well as C1. We make two
-observations: first, in all Apoints, it would be possible to
-fit entirely within the on-chip memory available on VEGA,
-exploiting the 4MB of non-volatile MRAM. This would allow
-avoiding any external memory access, increasing the energy
-efficiency of the algorithm by a factor of up to 3[20].
-Moreover, considering that the maximization of accuracy is
-often the primary objective in CL, we observe that accumu-
-lating features at l= 19 with 1500 UINT-8 LRs (point C1)
-enables accuracy to grow above 77%, almost 10% more than
-the compact solutions in A(Fig. 7). This analysis allows us to
-also speculate over possible future architectural explorations to
-design optimized bottleneck layers that could facilitate better
-memory accuracy trade-off for QLR-CL.
-C. Hardware/Software Efficiency
-To assess the performance of the proposed solution, we
-study the efficiency of the CL Software primitives on the target
-platform and the sensitivity to some of the HW architecturalparameters, namely the #cores, the L1 memory size and the
-cluster DMA Bandwidth.
-Single-tile performance on L1 TCDM: Based on the tiling
-strategy described in Section IV-B, we run experiments con-
-cerning the CL primitives of the software stack that operates
-on individual tiles of data placed in the L1 memory. Figure 8
-shows the latency performance, expressed as MAC/cyc , i.e.
-the ratio between Multiply-Accumulate operations ( MAC ) and
-elapsed clock cycles ( cyc), for each of the main FP32 compu-
-tation kernels in case of single-core ( 1-CORE ) or multi-core
-(2-4-8-CORES ) execution. We highlight that a higher value of
-MAC/cyc denotes a more efficient processing scheme, leading
-to lower latency for a given computation workload, i.e. fixed
-MAC. More specifically, in this plot, we evaluate the forward
-(FW), backward error ( BW ERR ), and backward gradient ( BW
-GRAD ) for each of the considered layer for a varying size of
-the L1 TCDM memory, i.e. 128, 256 or 512kB. The shapes
-of the tiles for PointWise ( PW), DepthWise ( DW), and Linear
-(Lin) layers used for the experiments are reported in the tables
-on the left of the figure. Such dimensions are defined to fit
-three different sizes of the TCDM, considering buffers of size
-64kB, 128kB and 256kB.
-Focusing firstly on the PW layers (histograms at the top
-of the figure), we observe a peak performance in the 8-cores
-FW step, achieving up to 1.91 MAC/cyc for a L1 memory
-size of 512kB. We observe also a performance improvement
-of up to 11% by increasing the L1 size from 128kB to 512kB,
-which is is motivated by the higher computational density of
-the kernel: if L1 = 512 kB the inner loop features 4 iterations
-than a scenario with 128kB of L1 size. Moreover, the parallel
-speedup scales almost linearly with respect to the number of
-cores and archives 7.2 in case of 8 cores. With respect to
-the theoretical maximum of 8 , the parallel implementation
-presents some overheads mainly due to increased L1 TCDM
-contentions and cluster’s cache misses.
-If we look at DW convolutions, their performance is lower
-with respect to the others. The main reason is that it requires
-a software-based im2col data layout transformation, which
-increase the amount of data marshaling operations and adds an
-extra L1 buffer, thus reducing the size of matrices in the matrix
-Fig. 6. Accuracy achieved by considering NLR=f750;1500;3000gand
-different precision, highlighting the Pareto frontier.10
-Fig. 7. Memory requirements for the points highlighted in Fig. 6. Each layer
-belongs to the Pareto frontier and accounts for all the memory components.
-Going deeper into the network, LRs (gray) dominate memory consumption.
-The other components are the parameters of the frozen stage, the gradient and
-the activations needed during the training.
-multiplication, leading to increased overheads. Specifically, we
-measure the workload of the im2col to achieve up to 70%
-of the FW kernel’s latency. As mentioned in Section IV, the
-primitives we introduce also support performing the im2col
-directly when moving the data tile from L2 via DMA transfer –
-in that case, this source of performance loss is not present, and
-the MAC/cyc necessary for depthwise convolutions increases
-up to 1 MAC/cycles for depthwise forward-prop, depending
-also on the L1 size selected. The remaining overhead with
-respect to pointwise convolutions is justified by the fact that
-depthwise convolutions can only exploit filter reuse (of size
-33, for example, in MobileNet-V1 DW layers) and no input
-channel data-reuse, resulting in much shorter inner loops and
-more visible effect of overheads. This latter effect cannot be
-counteracted by efficient DMA usage; on the other hand, since
-depthwise convolutions account for less than 1:5% of the
-computation, their impact on the overall latency is limited,
-as we further explore in the following section.
-Moving our analysis towards the different performance
-between forward- and backward-prop layers (particularly BW
-grad), we observe that this effect is again due to different
-data re-use between the matrix multiplication kernels. The
-reduction in re-use in the backward-prop is due to the tiling
-strategy adopted (see Fig. 3) has a grad output vector which
-is shorter than the input in the forward matrix multiplication.
-Specifically, the input to the matrix multiplication has size
-8x1x1 in backward, while the input shape in forward changes
-accordingly with the L1 memory: 512x1x1 for 128kB L1,
-1024x1x1 for 256kB L1 and 2048 for 512kB L1. In this
-scenario, the inner loop of the matrix multiplication of a
-forward computation is 64 , 128or 256larger with
-respect to the backward kernels’ cases. This fact motivates the
-lower MAC/cyc of the BW ERR step (22%) and BW GRAD
-step (-46%) if compared to the FW kernel.
-L2-L1 DMA Bandwidth effects on performance: Next we
-analyze the impact of L2-L1 DMA Bandwidth variations, due
-to the Cluster DMA, on the overall performance of the learning
-task. In particular, we monitor the latency and the MAC/cyc
-for multiple values of L2-L1 bandwidth ranging from 8 to
-128 bits per clock cycle (bit/cyc) and different configurations
-of #cores and L1 size. We remark that a higher value of
-MAC/cyc indicates a better performing HW configuration. Ouranalysis assumes a single half-duplex DMA channel, hence the
-bandwidth value accounts for either read or write transfers.
-Fig. 9 reports the average MAC/cyc when running the
-forward and backward steps with respect to the L2-L1 cluster’s
-DMA bandwidth. As a benchmark, we consider the adaptive
-stage of the MobileNetV1 model when the LR layer is set to
-the 19th layer. Hence, we adopt our tiling strategy and double-
-buffering scheme to realize the training. When increasing
-the L1 size, the tensor tiles feature a larger size, therefore
-demanding a higher transfer time to copy data between the
-L1 memory (used for computation) and L2 memory (used for
-storage). Thanks to the adopted double-buffering technique,
-such transfer time can be hidden by the computation time
-because the DMA works in the background of CPU operation
-(compute-bound ). On the contrary, if the transfer time results
-dominating, the computation becomes DMA transfer-bound ,
-with lower benefits from the multi-core acceleration.
-In case of single core execution, the measured MAC/cyc
-does not vary with respect to the L1 size (128kB, 256kB or
-512kB) as can be seen from the plot. In this scenario, the CPU
-time results as the dominant contribution with respect to the
-transfer time: the execution is compute-bound and a higher
-L2-L1 bandwidth does not impact the overall performance.
-Differently, in a multi-core execution (2, 4 or 8 cores), the
-average MAC/cyc increases and therefore the ratio between
-transfer time and the computation time decreases: from the plot
-we can observe higher performance if the DMA bandwidth is
-increased. If featuring a L1 size of 128kB, the sweet spots
-between DMA and compute bound are observed when the
-L2-L1 DMA bandwidth is 16 (2 cores), 32 (4 cores) and 64
-(8 cores) bit/cyc, respectively, as highlighted by the red circles
-in the plot. These configurations denote the sweet spots to tune
-the DMA requirements with respect to the chosen L1 memory
-size and #cores.
-If focusing more on the impact of the L1 memory size to
-the multi-core performance, we observe up to 2 efficiency
-gain with 8 cores with a larger L1 memory, increasing from
-0.25 MAC/cyc for a 128kB L1 memory to 0.4MAC/cyc at
-L1=256kB and to 0.53MAC/cyc for 512kB of L1. At 64
-bit/cyc of L2-L1 DMA bandwidth, the execution, which is
-dominated by the computation, reaches 0.52MAC/cyc, 2.12 
-faster than the low-bandwidth configuration.
-From this analysis we can conclude that the best design
-point for the learning task on a low-end multi-core architecture
-can be pinpointed leveraging the L2-L1 DMA Bandwidth and
-the L1 memory size tuning: when using 8 cores, 128kB of
-L1 memory, which is typically the main expensive resource
-for the system, can lead already to the highest performance as
-long as the DMA features a bandwidth of 64 bit/cyc. On the
-contrary, if the DMA’s bandwidth is as low as 8 bit/cyc, a 512
-kB L1 memory is needed to gain maximum performance. The
-target chip VEGA includes a L1 memory of 128 kB; the DMA
-follows a full-duplex scheme and can provide up to 64 bit/cyc
-for read transactions and 64 bit/cyc for write transactions.
-Therefore the VEGA HW architecture can fully exploit the
-presented SW architecture and optimization schemes to reach
-the optimal utilization and performance for the learning task.11
-Fig. 8. Efficiency, expressed in MAC/cyc, of the proposed CL primitives for forward and backward pass: PointWise, DepthWise, and Linear layers. The
-analysis concerns a varying number of cores (1, 2, 4 or 8) and L1 memory size (128, 256 or 512 kB), which impacts on the dimension of the layer’s tensor
-tiles as reported in the tables on the left.
-Fig. 9. SW efficiency, expressed as average MAC/cyc, when running forward
-and backward steps with respect to a varying L1-L2 bandwidth. Every line
-corresponds to a configuration of #cores (1, 2, 4 or 8 cores) and L1 memory
-size (128, 256 or 512kB).
-D. Latency Evaluation on VEGA SoC
-We run experiments on the VEGA SoC to assess the on-
-device learning performance, in terms of latency and energy
-consumption, of the proposed QLR-CL framework. Specifi-
-cally, we report the computation time, i.e. the latency, at the
-running frequency of 375MHz and the power consumption by
-measuring the current absorbed by the chip when powered at
-1.8V . To measure the full layer latency, we profile forward
-and backward tiled kernels, which include DMA transfers
-of data, initially stored in L2, and calls to low-level kernel
-primitives, introduced above. On average, we observe a 7% of
-tiling overhead with respect to the single-tile execution on L1.
-This is not surprising, due to the large bandwidth availability
-between L1 and L2 and the presence of compute-bound matrix
-multiplication operations.TABLE IV
-CUMULATIVE LATENCY VALUES PER LEARNING EVENT FOR VEGA,
-STM32, AND SNAPDRAGON 845.
-LR VEGA @ 375 MHz STM32L4 @ 80 MHz Snapdragon
-Layer Adaptive Frozen Cumul. Total Cumul. Total
-l [s] [s] En. [J] [s] En. [J] [s]
-20 2:49
1030.87 154 1:65
1055688 n.a.
-21 1:73
1030.94 107 1:15
1053981 n.a.
-22 1:64
1030.95 101 1:08
1053728 n.a.
-23 8:77
1021.03 54.3 5:86
1042020 n.a.
-24 7:81
1021.03 48.4 5:12
1041769 n.a.
-25 4:01
1021.09 24.9 2:65
104915 n.a.
-26 3:81
1021.10 23.5 2:49
104859 n.a.
-27 2.07 1.25 0.13 1:39
1024.80 0.50
-Based on the implemented tiled functions, we report the
-layer-wise performance in Table IV for any of the layers of
-the MobileNet-V1 model. We consider as complete time for
-the execution of a layer the cumulated time for frozen stage
-andadaptive stage . The latency of the frozen stage is obtained
-using DORY [39] to deploy the network, as this operation is
-performed as pure 8-bit quantized inference. We compute the
-full latency of the adaptive stage as the time needed to execute
-the forward and backward phases of each layer. Since we have
-multiple configurations, latencies for retraining start growing
-from the last layer (#27) up to layer #20, where retraining
-comprises a total of eight layers.
-First of all, we note that frozen stage latencies are utterly
-dominated by the adaptive stage . Apart from the faster infer-
-ence backend, which can rely on 8-bit SIMD vectorization,
-this is because only 21 images per mini-batch pass through
-thefrozen stage , while the adaptive stage has to be executed
-on 128 latent inputs (107 LRs and the 21 dequantized outputs
-from the frozen stage ), and it has to run for multiple epochs
-(by default, 4) in order to work.
-When l= 27 , the adaptive stage is very fast thanks to12
-its very small number of parameters (it produces just the 50
-output classes). This is the only case in which the frozen
-stage is non-negligible ( 1/6 of the overall time). Progressing
-upward in the table, the frozen stage becomes negligible. The
-cumulative impact of forward and backward passes through
-all the other layers we take intoaccount ( lfrom #20 to #26) is
-in the range between 0.3h and 1.5h. In particular, l= 23
-corresponds to14 min per learning event; this LR layer
-corresponds to high accuracy ( >75% in Core50, see Fig. 6),
-which means that in this time the proposed system is capable
-of acquiring a very significant new capability (e.g., a new
-task/object to classify) while retaining previous knowledge to
-a high degree.
-Having the basic mini-batch measurements, we can estimate
-any scenario, by considering that to train with 1500 LR and
-l= 27 , we will need 300 new images, thus we need 14 mini-
-batches ( 300=21), which leads to 3.30 seconds to learn a new
-set of images, with an accuracy of 69.2%. If we push back
-the LR layer l, this leads to an increase of accuracy 76.5%,
-at the expense of much larger latency, up to 42 minutes for
-layer #20 (see Table IV).
-E. Energy Evaluation on CL Use-Cases and Comparison with
-other Solutions
-To understand the performance of our system and its real-
-world applicability, we study two use-cases: a single mini-
-batch of the Core50 training we used, and the simplified sce-
-nario presented by Pellegrini et al. [1] in their demonstration
-video. We compare our results with another MCU targeting
-ultra-power consumption: a NUCLEO-64 board based on the
-STM32L476RG MCU, on which we ran a direct port of the
-same code we use on the PULP-based platforms. It has two on-
-chip SRAMs with 1-cycle access time and an overall capacity
-of 96kB. Performance results, in terms of latency, are reported
-in Table IV, where we take intoaccount the cumulative latency
-values both for VEGA and STM32 implementations, along
-with the cumulative energy consumption. Cumulative latency
-is computed by adding from the linear layer of the network
-the latencies of the preceding layers.
-On average, execution on VEGA’s 8-cores on performs
-65faster with respect to the STM32 solution thanks to
-three main factors. Firstly, the clock frequency of VEGA is
-4.7higher than the max clock frequency of the STM32L4
-(375MHz vs 80MHz), also thanks to the superior technology
-node. Secondly, VEGA presents a parallel speed-up of up
-to 7.2. Lastly, thanks to the more optimized ISA and the
-core microarchitecture, VEGA performs less operations while
-executing the same learning task. For example, the inner
-loop of the matrix multiplication on VEGA requires only 4
-instructions while the STM32L4 takes 9 instructions, resulting
-2.25faster, mainly thanks to the HW loop extension and the
-fmadd.s instruction.
-The latency speed up, leads to an energy gain of around
-37, because the average power consumption of VEGA is
-2higher than the STM32L4 at full load.
-Notice that the latency measurement of the STM32L4 does
-not account for possible overheads due to the tiling data
-Fig. 10. Battery Lifetime of the VEGA SoC and the STM32L4 devices when
-handling multiple learning events per hour.
-between the small on-chip SRAM banks and off-chip memory.
-Even then, our results show that fine-tuning from any layer
-above the last one results in too large a latency to be realistic
-on STM32L4 – in the order of a day per learning event with
-l= 23 . On the contrary, CL on VEGA can be completed in
-14minutes if selecting l= 23 or as fast as 3.3 seconds if
-retraining only the last layer.
-Given the reported energy consumption, we estimated the
-battery lifetime of our device when adapting the inference
-model by means of multiple learning events per hour; we
-assumed no extra energy consumption for the remaining
-time. In particular, Fig. 10 shows the battery lifetime (in
-hours) depending on the selected Latent Replay layer and the
-adaptation rate, expressed as the amount of learning events
-per hour. We considered a 3300 mAh battery as the only
-energy source for the device. By retraining only the last layer
-(LR= 27 ), an intelligent node featuring our device can perform
-more than 1080 continual learning events per hour, leading
-to a lifetime of about 175h. On the contrary, if retraining
-larger portions of the network, the training time increases
-and the maximum rate of the learning events reduces to less
-than 10/hour, with a lifetime in the range 200-1000h. In
-comparison, on a STM32L4, if retraining the coefficients of
-the last layer, the maximum learning rate per hour is limited to
-750, with a lifetime of about 10h. This latter can be increased
-up to 10000h but retraining only once in one hour. At the
-same learning event rate, the battery lifetime of VEGA is 20x
-higher.
-Lastly, we compare with the use-case presented by Pel-
-legrini et al. [1], where they developed a mobile phone
-application that performs CL with LRs on a OnePlus6 with
-Snapdragon845. For this scenario, they consider only 500 LRs
-before the linear layer, these will be shuffled with 100 new
-images. Then, by construction the mini-batch is composed of
-100 LRs and 20 new images, thus, for each of the 8 training
-epochs, the network will process 5 times over the 20 new
-images and the 100 LRs. This scenario leads them to obtain
-an average latency of 502 ms for a single learning event. On
-the other hand, considering our measurements on VEGA we
-obtain a forward latency of 1.25s and a training time of 2.07s
-for a whole learning event.
-Considering the power envelope of a Snapdragon845 of13
-about 4W, and the average power consumption of VEGA of
-62mW, this implies that our solution is 9.7 more efficient in
-terms of energy. We additionally assess the energy consump-
-tion and the duration of a battery in the mobile application
-scenario, provided the energy measurements on VEGA, when
-using a 3300mAh battery. Thus, if we consider performing
-learning over a mini-batch of images once every minute in
-the ultra-fast scenario (just retraining the linear layer) and
-to perform an inference each second, we obtain an energy
-consumption of 0.25J per minute. This leads the accuracy of
-the model to achieve an average of 69.2%, with an overall
-lifetime of about 108 days.
-VI. C ONCLUSION
-In this work, we presented what, to the best of our knowl-
-edge, is the first HW/SW platform for TinyML Continual
-Learning – together with the novel Quantized Latent Replay-
-based Continual Learning (QLR-CL) methodology. More
-specifically, we propose to use low-bitwidth quantization to
-reduce the high memory requirements of a Continual Learning
-strategy based on Latent Replay rehearsing. We show a small
-accuracy drop as small as 0.26% if using 8-bit quantized LR
-memory if compared to floating-point vectors and an average
-degradation of 5% if lowering the bit precision to 7-bit,
-depending on the LR layer selected. Our results demonstrate
-that sophisticated adaptive behavior based on CL is within
-reach for next-generation TinyML devices, such as PULP
-devices; we show the capability to learn a new Core50 class
-with accuracy up to 77%, using less than 64MB of memory –
-a typical constraint to fit Flash memories. We show that our
-QLR-CL library based on VEGA achieves up to 65better
-performance than a conventional STM32 microcontroller.
-These results constitute an initial step towards moving the
-TinyML from a strict train-then-deploy approach to a more
-flexible and adaptive scenario, where low power devices are
-capable to learn and adapt to changing tasks and conditions
-directly in the field.
-Despite this work focused on a single CL method, we
-remark that, thanks to the flexibility of the proposed platform,
-other adaptation methods or models can be also supported,
-especially if relying on the back-propagation algorithm and
-CNN primitives, such as convolution operations.
-ACKNOWLEDGEMENT
-We thank Vincenzo Lomonaco and Lorenzo Pellegrini for
-the insightful discussions.
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-Leonardo Ravaglia receive his M.Sc. degree in
-Automation Engineering from the University of
-Bologna in 2019. He is currently a Doctoral Student
-in Data Science and Computation at the University
-of Bologna. His research interests include DNN al-
-gorithms for Continual Learning, parallel computing
-on Ultra Low Power devices and Quantized Neural
-Networks.
-Dr. Manuele Rusci received the Ph.D. degree
-in electronic engineering from the University of
-Bologna in 2018. He is currently a Post-Doctoral
-Researcher at the same University at the Department
-of Electrical, Electronic and Information Engineer-
-ing “Guglielmo Marconi” and closely collaborates
-with Greenwaves Technologies. His main research
-interests include low-power embedded systems and
-AI-powered smart sensors.
-Davide Nadalini Davide Nadalini received the
-M.Sc. degree in electronic engineering from the
-University of Bologna in 2021. Since then, he is
-a Ph.D. student at Politecnico di Torino. His main
-research topic is Hardware-Software co-design and
-optimization of embedded Artificial Intelligence. His
-research interests include parallel computing, Quan-
-tized Neural Networks and low-level optimization.
-Dr. Alessandro Capotondi Dr. Alessandro Capo-
-tondi (IEEE Member) is a postdoctoral researcher
-at the Universit `a di Modena e Reggio Emilia (IT).
-His main research interests focus on heteroge-
-neous architectures, parallel programming models,
-and TinyML. He received his Ph.D. in Electrical,
-Electronic, and Information Engineering from the
-University of Bologna in 2016.
-Prof. Francesco Conti received the Ph.D. in elec-
-tronic engineering from the University of Bologna,
-Italy, in 2016. He is currently an Assistant Professor
-in the DEI Department of the University of Bologna.
-From 2016 to 2020, he was a postdoctoral researcher
-at the Integrated Systems Laboratory of ETH Z ¨urich
-in the Digital Systems group. His research is focused
-on the development of deep learning based intelli-
-gence on top of ultra-low power, ultra-energy effi-
-cient programmable Systems-on-Chip. In particular,
-he works on Deep Learning-aware architecture, on
-tinyML hardware acceleration facilities such as dedicated accelerator cores
-and ISA extensions, as well as on automated DNN architecture search, quan-
-tization, and deployment methodologies tuned to maximally exploit hardware
-features. He has been involved in the development of the RISC-V based open-
-source Parallel Ultra-Low-Power (PULP) project since its inception (2013).
-From 2020, he has collaborated with GreenWaves Technologies, France as a
-consultant for the development of DNN and RNN acceleration IP. His research
-has resulted in 50+ publications in international conferences and journals and
-has been awarded several times, including the 2020 IEEE TCAS-I Darlington
-Best Paper Award, the 2018 Hipeac Tech Transfer Award, the 2018 ESWEEK
-Best Paper Award, and the 2014 ASAP Best Paper Award.
-Prof. Luca Benini (Fellow, IEEE) received the
-Ph.D. degree in electrical engineering from Stanford
-University, Stanford, CA, USA, in 1997. He was
-the Chief Architect of the Platform 2012/STHORM
-Project with STMicroelectronics, Grenoble, France,
-from 2009 to 2013. He held visiting/consulting po-
-sitions with ´Ecole Polytechnique F ´ed´erale de Lau-
-sanne, Lausanne, Switzerland; Stanford University;
-and IMEC, Leuven, Belgium. He is currently a Full
-Professor with the University of Bologna, Bologna,
-Italy. He is also the Chair of Digital Circuits and
-Systems with ETH Z ¨urich, Z ¨urich, Switzerland. He has authored over 700
-papers in peer-reviewed international journals and conferences, four books,
-and several book chapters. His current research interests include energy-
-efficient system design and multicore system-on-chip design. Dr. Benini is
-a member of Academia Europaea.

txt/2110.12687.txt

@@ -1,427 +0,0 @@
-arXiv:2110.12687v1  [cs.CL]  25 Oct 2021Fine-tuning of Pre-trained Transformers for Hate, Offensi ve,
-and Profane Content Detection in English and Marathi
-Anna Glazkova1,*, Michael Kadantsev2, Maksim Glazkov3,
-1 University of Tyumen, Tyumen, Russia
-2 Thales Canada, Transportation Solutions, Toronto, Canad a
-3 Neuro.net, Nizhny Novgorod, Russia
-* [email protected]
-Abstract
-This paper describes neural models developed for the Hate Speech and Offensive Content
-Identification in English and Indo-Aryan Languages Shared Task 20 21. Our team called
-neuro-utmn-thales participated in two tasks on binary and fine-grained classification of
-English tweets that contain hate, offensive, and profane conten t (English Subtasks A &
-B) and one task on identification of problematic content in Marathi ( Marathi Subtask
-A). For English subtasks, we investigate the impact of additional co rpora for hate speech
-detection to fine-tune transformer models. We also apply a one-vs -rest approach based
-on Twitter-RoBERTa to discrimination between hate, profane and o ffensive posts. Our
-models ranked third in English Subtask A with the F1-score of 81.99% a nd ranked
-second in English Subtask B with the F1-score of 65.77%. For the Mar athi tasks, we
-propose a system based on the Language-Agnostic BERT Sentenc e Embedding (LaBSE).
-This model achieved the second result in Marathi Subtask A obtainin g an F1 of 88.08%.
-Introduction
-Social media has a greater impact on our society. Social networks g ive us almost limitless
-freedom of speech and contribute to the rapid dissemination of info rmation. However,
-these positive properties often lead to unhealthy usage of social m edia. Thus, hate
-speech spreading affects users’ psychological state, promote s violence, and reinforces
-hateful sentiments [4, 5]. This problem attracts many scholars to a pply modern tech-
-nologies in order to make social media safer. The Hate Speech and Of fensive Content
-Identification in English and Indo-Aryan Languages Shared Task (H ASOC) 2021 [27]
-aims to compare and analyze existing approaches to identifying hate speech not only
-for English, but also for other languages. It focused on detecting hate, offensive, and
-profane content in tweets, and offering six subtasks. We particip ated in three of them:
-•English Subtask A: identifying hate, offensive, and profane content from the
-post in English [24].
-•English Subtask B: discrimination between hate, profane, and offensive posts
-in English.
-•Marathi Subtask A: identifying hate, offensive, and profane content from the
-post in Marathi [14].
-1/10The source code for our models is freely available1.
-The paper is organized as follows. Section 2 contains a brief review of related works.
-Next, we describe our experiments on the binary and fine-grained c lassification of En-
-glish tweets in Section 3. In Section 4, we present our model for hat e, offensive, and
-profane language identification in Marathi. We conclude this paper in S ection 5. Finally,
-Section 6 contains acknowledgments.
-1 Related Works
-We briefly discuss works done related to harmful content detectio n in the past few
-years. Shared tasks related to hate speech and offensive langua ge detection from tweets
-was organized as a part of some workshops and conferences, suc h as FIRE [22, 23], Se-
-mEval, [3, 10], GermEval [40, 43], IberLEF [42], and OSACT [29]. The pa rticipants
-proposed a broad range of approaches from traditional machine le arning techniques
-(for example, Support Vector Machines [15, 38], Random Forest [34 ]) to various neural
-architectures (Convolutional Neural Networks, CNN [35]; Long Sh ort Term Memory,
-LSTM [26, 28]; Embeddings from Language Models, ELMo [6]; and Bidirec tional En-
-coder Representations from Transformers, BERT [19,36]). In mo st cases, BERT-based
-systems outperformed other approaches.
-Most research on hate speech detection continues to be based on English corpora.
-Despite this, the harmful content is distributed in different langua ges. Therefore, there
-have been previous attempts at creating corpora and developing m odels for hate speech
-detection in common non-English languages, such as Arabic [1, 29], G erman [22, 23, 40,
-43], Italian [7, 37], Spanish [3, 42], Hindi [22, 23], Tamil and Malayalam [22]. Several
-studies have focused on collecting hate speech corpora for Chines e [41], Portuguese [11],
-Polish [33], Turkish [8] and Russian [16] languages.
-2 English Subtasks A & B: Identification and Fine-
-grained Classification of Hate, Offensive, and Pro-
-fane Tweets
-The objective of English Subtasks A & B is to identify whether a tweet in English
-contains harmful content (Subtask A) and perform a fine-graine d classification of posts
-into three categories, including: hate, offensive, or profane (Su btask B).
-2.1 Data
-The dataset provided to the participants of the shared task cont ains 4355 manually
-annotated social media posts divided into training (3074) and test ( 1281) sets. Table 1
-presents the data description.
-Further, we tested several data sampling techniques using differ ent hate speech cor-
-pora as additional training data. Firstly, we evaluated the joint use of multilingual data
-provided by the organizers of HASOC 2021, including the English, the Hindi, and the
-Marathi training sets. Secondly, as the training sets were highly imb alanced, we applied
-the positive class random oversampling technique so that each train ing batch contained
-approximately the same number of samples. Besides, we experiment ed with the seq2seq-
-based data augmentation technique [17]. For this purpose, we fine- tuned the BART-base
-model for the denoising reconstruction task where 40% of tokens are masked and the
-goal of the decoder is to reconstruct the original sequence. Sinc e the BART model [18]
-1https://github.com/ixomaxip/hasoc
-2/10Table 1. Data description.
-Label DescriptionNumber of examples
-(training set)
-Subtask A
-NOTNon Hate-Offensive: the post does not contain
-hate speech, profane, offensive content1102
-HOFHate and Offensive: the post contains hate,
-offensive, or profane content.1972
-Subtask B
-NONEThe post does not contain hate speech, profane,
-offensive content1102
-HATE Hate speech: the post contains hate speech content. 542
-OFFN Offensive: the post contains offensive content. 482
-PRFN Profane: the post contains profane words. 948
-Table 2. Hate-related dataset characteristics.
-Dataset Size Labels
-HASOC 2020 4522HOF - 50.4%
-NOT - 49.6%
-HatebaseTwitter 24783hate speech - 20.15%
-offensive language - 85.98%
-neither - 23.77%
-HatEval 130001 (hate speech) - 42.08%
-0 (not hate speech) - 57.92%
-OLID 14100OFF - 32.91%
-NOT - 67.09%
-already contains the ¡mask¿ token, we use it to replace mask tokens . We generated
-one synthetic example for every tweet in the training set. Thus, th e augmented data
-size is the same size as the size of the original training set. Finally, we e valuated the
-impact of additional training data, including: (a) the English dataset , used at HASOC
-2020 [22]; (b) HatebaseTwitter, based on the hate speech lexicon f rom Hatebase2[10];
-(c) HatEval, a dataset presented at Semeval-2019 Task 5 [3] ; (d) Offensive Language
-Identification Dataset (OLID), used in the SemEval-2019 Task 6 (O ffensEval) [45]. All
-corpora except the HatebaseTwitter dataset contain non-inter sective classes. Besides,
-all listed datasets are collected from Twitter. A representative sa mpling of additional
-data is shown in Table 2.
-We preprocessed the datasets for Subtasks A & B in a similar manner . Inspired
-by [2], we used the following text preprocessing technique3: (a) removed all URLs; (b)
-replaced all user mentions with the $MENTION $placeholder.
-2.2 Models
-We conduct our experiments with neural models based on BERT [12] a s they have
-achieved state-of-the-art results in harmful content detectio n. For example, BERT-
-based models proved efficient at previous HASOC shared tasks [22 , 23] and SemEval
-[32,45].
-We used the following models:
-2https://hatebase.org/
-3https://pypi.org/project/tweet-preprocessor
-3/10Table 3. Model validation results for English Subtask A, %.
-Model F1 P R
-BERT 79.24 79.74 78.82
-BERTweet 78.65 79.36 78.08
-Twitter-RoBERTa 81.1 80.01 82.65
-LaBSE (English dataset) 78.83 79.5 78.29
-LaBSE (English + Hindi) 79.32 79.95 78.8
-LaBSE (English, Hindi, and Marathi) 79.27 81.74 77.79
-Adding extra data to Twitter-RoBERTa
-+ random oversampling 79.97 79.9 80.04
-+ BART data augmentation 79.24 78.44 80.31
-+ HASOC 2020 78.79 77.66 80.47
-+ HatabaseTwitter 81.19 79.99 82.93
-+ HatEval 74.31 75.53 73.64
-+ OLID 79.29 78.17 80.93
-•BERT base[12], a pre-trained model on BookCorpus [46] and English Wikipedia
-using a masked language modeling objective.
-•BERTweet base[30], a pre-trained language model for English tweets. The corpus
-used to pre-train BERTweet consists of 850M English Tweets includin g 845M
-Tweets streamed from 01/2012 to 08/2019 and 5M Tweets related to the COVID-
-19 pandemic.
-•Twitter-RoBERTa basefor Hate Speech Detection [2], a RoBERTa base[20] model
-trained on 58M tweets and fine-tuned for hate speech detection w ith the Tweet-
-Eval benchmark.
-•LaBSE [13], a language-agnostic BERT sentence embedding model su pporting 109
-languages.
-2.3 Experiments
-For both Subtask A and Subtask B, we adopted pre-trained models from HuggingFace
-[44] and fine-tuned them using PyTorch [31]. We fine-tuned each pre -trained language
-model for 3 epochs with the learning rate of 2e-5 using the AdamW op timizer [21]. We
-set batch size to 32 and maximum sequence size to 64. To validate our models during
-the development phase, we divided labelled data using the train and va lidation split in
-the ratio 80:20.
-Table 3 shows the performance of our models on the validation subse t for Subtask A
-in terms of macro-averaging F1-score (F1), precision (P), and re call (R). As can be seen
-from the table, BERT, BERTweet, and LaBSE show very close result s during validation.
-Despite this, LaBSE jointly fine-tuned on three mixed multilingual dat asets shows the
-highest precision score. The use of Twitter-RoBERTa increases th e F1-score by 1.5-2.5%
-compared to other classification models. Based on this, we chose Tw itter-RoBERTa for
-further experiments. We found out that neither the random over sampling technique
-nor the use of the augmented and additional data shows a perform ance improvement,
-except the joint use of the original dataset and the HatebaseTwit ter dataset that gives
-an F1-score growth of 0.09% and a precision growth of 0.28% compar ed to basic Twitter-
-RoBERTa.
-For our official submission for Subtask A, we designed a soft-votin g ensemble of five
-Twitter-RoBERTa jointly fine-tuned on the original training set and the HatebaseTwit-
-4/10Table 4. Performance of our final models for English Subtasks A & B, official results,
-%.
-SubtaskF1 (our
-model)F1 (winning
-solution)P (our
-model)P (winning
-solution)Avg F1Number of
-submitted
-teamsRank
-A 81.99 83.05 84.68 84.14 75.7 56 3
-B 65.77 66.57 66.32 66.88 57.07 37 2
-ter dataset (see Table 4). For Subtask B, we used the following one -vs-rest approach to
-discrimination between hate, profane, and offensive posts.
-•First, we applied our Subtask A binary models to identify non hate-of fensive
-examples.
-•Second, we fine-tuned three Twitter-RoBERTa binary models to de limit exam-
-ples of hate-vs-profane, hate-vs-offensive, and offensive-v s-profane classes. The
-training dataset was extended with the HatebaseTwitter dataset .
-•Finally, we compared the results of binary models. If the result was d efined
-uniquely, we used it as a predicted label. Otherwise, we chose the labe l in propor-
-tion to the number of examples in the training set.
-This can be illustrated briefly by the following examples.
-–Let the models show the following results:
-∗hate-vs-profane →hate;
-∗hate-vs-offensive →hate;
-∗offensive-vs-profane →offensive.
-Thus, classes have the following votes: hate – 2, offensive - 1, pro fane – 0.
-Then we predict the HATE label.
-–If the results are:
-∗hate-vs-profane →profane;
-∗hate-vs-offensive →hate;
-∗offensive-vs-profane →offensive,
-we have the class votes, such as hate – 1, offensive - 1, profane – 1. Then we
-choose the PRFN label as the most common label in the training set.
-3 Marathi Subtask A: Identifying Hate, Offensive,
-and Profane Content from the Post
-3.1 Data
-For the Marathi task, we used the original training and test sets p rovided by the orga-
-nizers of the HASOC 2021. The whole dataset contains 2499 tweets , including: 1874
-training and 625 test examples. The training set consists of 1205 te xts of the NOT
-class and 669 texts of the HOF class. We used raw data as an input fo r our models.
-Following [25,39], we experimented with the combination of the English, the Hindi, and
-the Marathi training sets provided by the organizers.
-5/10Table 5. Model validation results for Marathi Subtask A, %.
-Model F1 P R
-XLM-RoBERTa (Marathi dataset) 83.87 85.39 83.39
-XLM-RoBERTa (Marathi + Hindi) 83.23 83.82 82.76
-XLM-RoBERTa (Marathi + English) 84.83 85.03 84.64
-XLM-RoBERTa (Marathi + Hindi + English) 84.35 84.82 83.95
-LaBSE (Marathi) 87.76 87.82 87.68
-LaBSE (Marathi + Hindi) 87.62 88.21 87.13
-LaBSE (Marathi + English) 87.62 88.21 87.13
-LaBSE (Marathi + Hindi + English) 86.34 86.63 86.08
-Table 6. Performance of our final model for the Marathi Subtask A, offic ial results, %.
-F1 (our
-model)F1 (winning
-solution)P (our
-model)P (winning
-solution)Avg F1Number of
-submitted
-teamsRank
-88.08 91.44 87.58 91.82 82.55 25 2
-3.2 Models
-We evaluated the following models:
-•XLM-RoBERTa base[9], a transformer-based multilingual masked language model
-supporting 100 languages.
-•LaBSE [13], a language-agnostic BERT sentence embedding model pr e-trained on
-texts in 109 languages.
-3.3 Experiments
-We experimented with the above-mentioned language models fine-tu ned on monolingual
-and multilingual data. For model evaluation during the development p hase, we used
-the random train and validation split in the ratio 80:20 with a fixed seed. We set the
-same model parameters as for English tasks.
-Table 5 illustrates the results. It can be seen that LaBSE outperfo rms XLM-
-RoBERTa in all cases. Moreover, the F1-score of LaBSE fine-tune d only on the Marathi
-dataset are higher than the results of LaBSE fine-tuned on multiling ual data. XLM-
-RoBERTa, on the other hand, mostly benefits from multilingual fine- tuning.
-For our final submission, we used a soft-voting ensemble of five LaB SE fine-tuned on
-the official Marathi dataset provided by the organizers of the co mpetition. The results
-of this model on the test set are shown in Table 6.
-Conclusion
-In this paper, we have presented the details about our participatio n in the HASOC
-Shared Task 2021. We have explored an application of domain-specif ic monolingual and
-multilingual BERT-based models to the tasks of binary and fine-grain ed classification of
-Twitter posts. We also proposed a one-vs-rest approach to discr imination between hate,
-offensive, and profane tweets. Further research can focus on analyzing the effectiveness
-of various text preprocessing techniques for harmful content d etection and exploring
-how different transfer learning approaches can affect classifica tion performance.
-6/10Acknowledgments
-The work on multi-label text classification was carried out by Anna Gla zkova and sup-
-ported by the grant of the President of the Russian Federation no . MK-637.2020.9.
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txt/2111.09172.txt

@@ -1,536 +0,0 @@
-End-to-end optimized image compression with competition of prior distributions
-Benoit Brummer
-intoPIX
-Mont-Saint-Guibert, Belgium
-[email protected] De Vleeschouwer
-Universit ´e catholique de Louvain
-Louvain-la-Neuve, Belgium
-[email protected]
-Abstract
-Convolutional autoencoders are now at the forefront of
-image compression research. To improve their entropy cod-
-ing, encoder output is typically analyzed with a second
-autoencoder to generate per-variable parametrized prior
-probability distributions. We instead propose a compression
-scheme that uses a single convolutional autoencoder and
-multiple learned prior distributions working as a competition
-of experts. Trained prior distributions are stored in a static
-table of cumulative distribution functions. During inference,
-this table is used by an entropy coder as a look-up-table
-to determine the best prior for each spatial location. Our
-method offers rate-distortion performance comparable to
-that obtained with a predicted parametrized prior with only
-a fraction of its entropy coding and decoding complexity.
-1. Introduction
-Image compression typically consists of a transforma-
-tion step (including quantization) and an entropy coding
-step that attempts to capture the probability distribution of
-a transformed context to generate a smaller compressed bit-
-stream. Entropy coding ranges in complexity from simple
-non-adaptive encoders [ 26,24] to complex arithmetic coders
-with adaptive context models [ 15,23]. The entropy cod-
-ing strategy has been revised to address the specificities of
-learned compression. More specifically, for recent works
-that make use of a convolutional autoencoder [ 12] (AE) as
-the all-inclusive transformation and quantization step, the en-
-tropy coder relies on a cumulative probability model (CPM)
-trained alongside the AE [ 5]. This model estimates the cumu-
-lative distribution function (CDF) of each channel coming
-out of the AE and passes these learned CDFs to an entropy
-coder such as range encoding [16].
-Such a simple method outperforms traditional codecs
-like JPEG2000 but work is still needed to surpass complex
-codecs like BPG. Johannes Ball ´e et al. (2018) [ 6] proposed
-analyzing the output of the convolutional encoder with an-
-other AE to generate a floating-point scale parameter thatdiffers for every variable that needs to be encoded by the
-entropy coder, thus for every location in every channel. This
-method has been widely used in subsequent works but in-
-troduces substantial complexity in the entropy coding step
-because a different CDF is needed to encode every variable
-in the latent representation of the image, whereas the single
-AE method by Ball ´e et al. (2017) [ 5] reused the same CDF
-table for every latent spatial location.
-Our work uses the principle of competition of experts
-[22,14] to get the best out of both worlds. Multiple prior
-distributions compete for the lowest bit cost on every spatial
-location in the quantized latent representation. During train-
-ing, only the best prior distribution is updated in each spatial
-location, further improving the prior distributions special-
-ization. CDF tables are fixed at the end of training. Hence,
-at testing, the CDF table resulting in the lowest bitcost is
-assigned to each spatial location of the latent representation.
-The rate-distortion (RD) performance obtained is compa-
-rable to that obtained with a parametrized distribution [ 6],
-yet the entropy coding process is greatly simplified since it
-does not require a per-variable CDF and can build on look-
-up-tables (LUT) rather than the computation of analytical
-distributions.
-2. Background
-Entropy coders such as range encoding [ 16] require cdfs
-where, for each variable to be encoded, the probability that a
-smaller or equal value appears is defined for every allowable
-value in the latent representation space. Johannes Ball ´e et
-al.’s seminal work (2017) [ 5] consists of an AE, computing a
-latent image representation consisting in CLchannels of size
-HLWL, and a CPM, consisting of one CDF per latent out-
-put channel, which are trained conjointly. The latent repre-
-sentation coming out of the encoder is quantized then passed
-through the CPM. The CPM defines, in a parametrized and
-differentiable manner, a CDF per channel. At the end of
-training, the CPM is evaluated at every possible value1to
-generate the static CDF table. The CDF table is not differen-
-tiable, but going from a differentiable CPM to a static CDF
-table speeds up the encoding and decoding process. The
-1arXiv:2111.09172v1  [eess.IV]  17 Nov 2021CDF table is used to compress latent representations with an
-entropy coder, the approximate bit cost of a symbol is the
-binary logarithm of its probability.
-Ball´e et al. (2018) improved the RD efficiency by re-
-placing the unique CDF table with a Gaussian distribution
-parametrized with a hyperprior (HP) sub-network [ 6]. The
-HP generates a scale parameter, and in turn a different CDF,
-for every variable to be encoded. Thus, complexity is added
-by exploiting the parametrized Gaussian prior during the
-entropy coding process, since a different CDF is required for
-each variable in the channel and spatial dimensions.
-Minnen et al. proposed a scheme where one of multi-
-ple probability distributions is chosen to adapt the entropy
-model locally [ 21]. However, these distributions are defined
-a posteriori, given the encoder trained with a global entropy
-model. Thus [ 21] does not perform as well as the HP scheme
-[6] per [ 19, Fig. 2a]. In contrast, the present method jointly
-optimizes the local entropy models and the AE in an end-to-
-end fashion that results in greater performance. Minnen et al.
-[19] later proposed to improve RD with the use of an autore-
-gressive sequential context model. However, as highlighted
-in [13], this is obtained at the cost of increased runtime
-by several orders of magnitude. Subsequent works have
-attempted to reduce complexity of the neural network archi-
-tecture [ 10] and to bridge the RD gap with Minnen’s work
-[13], but entropy coding complexity has remained largely
-unaddressed and has instead evolved towards increased com-
-plexity [ 19,7,20] compared to [ 6]. The present work builds
-on Ball ´e et al. (2017) [ 5] and achieves the performance of
-Ball´e et al. (2018) [ 6] without the complexity introduced
-by a per-variable parametrized probability distribution. We
-chose Ball ´e et al. (2017) as a baseline because it corresponds
-to the basic unit adopted as a common reference and starting
-point for most models proposed in the recent literature to im-
-prove compression quality [ 6,19,13,20]. Due to its generic
-nature, our contribution remains relevant for the newer, often
-computationally more complex, incremental improvements
-on Ball ´e et al. (2017).
-3. Competition of prior distributions
-Our proposed method introduces competitions of expert
-[22,14] prior distributions: a single AE transforms the image
-and a set of prior distributions are trained to model the CDF
-of the latent representation in each spatial location. For each
-latent spatial dimension the CDF table which minimizes
-bit cost is selected; that prior is either further optimized
-on the features it won in the training mode, or its index is
-stored for decoding in the inference mode. This scheme is
-illustrated in Figure 1, a set of 16 optimized CDF tables is
-shown in Figure 2, and three sample images are segmented
-by “winning” CDF table in Figure 3.
-All prior distributions are estimated in parallel by consid-
-ering NCDFCDF tables, and selecting, as a function of the
-CDF0...
-CDFNCDF...Cumulative probability model
-Input image
-Encoder
-Decoder
-Reconstruction
-Distortion
-(eg: MSE, MS-SSIM, discriminator)x̂
-bitstream[ ŷk,l]ik,lbitstream
-bitCost( ŷk,l,         )BackpropagateBackpropagate
-Entropy coder
-ik,l=argmin p
-[bitCost( ŷk,l, CDF p)]...y x
-ŷ
-CDFik,l  
-Quantizer
-Noise (train),
-round (test) ŷk,lFor all k,l
-CDFik,l
-CDFik,lFigure 1. AE compression scheme with competition of prior distri-
-butions. The AE architecture is detailed in [ 6, Fig. 4]. The indices
-idenote the indices of CDF tables that minimizes the bitcount for
-each latent spatial dimension. Loss = Distortion + bitCost.
-0.00.51.0
-0.00.51.0
-0.00.51.0
-100
- 0 1000.00.51.0
-100
- 0 100 100
- 0 100 100
- 0 100
-Figure 2. We observe some diversity among the 16 cumulative
-distribution functions learned by a network trained with MSE loss
-and= 4096 . Each box presents a CDF table and each colored
-line corresponds to the cdfof one of 256 latent channels. The best
-fitting CDF table is selected for each latent spatial location.
-encoded latent spatial location, the one that minimizes the
-entropy coder bitcount. The CDF table index is determined
-for each spatial location by evaluating each CDF table in
-inference. This can be done in a vectorized operation given
-sufficient memory. During training the CPM is evaluated
-instead of CDF tables such that the probabilities are up to
-date and the model is differentiable, and the bit cost is re-
-turned as it contributes to the loss function. The cost of CDF
-table indices has been shown to be neglectable due to the
-reasonably small number of priors, which in turns results
-from the fact that little gain in latent code entropy has been
-obtained by increasing the number of priors.
-In all our experiments , the AE architecture follows the
-one in Ball ´e et al. (2018) [ 6], without the HP, since we found
-that the AE from [ 6] offers better RD than the one describedFigure 3. Segmentation of three test images [ 1]: each distinct color
-represents one of 64 CDF tables used to encode a latent spatial
-location ( 1616pixels patch)
-in Ball ´e et al. (2017) [ 5], even with a single CDF table. A
-functional training loop is described in Algorithm 1.
-Algorithm 1 Training loop
-y model.Encoder( x)
-ˆy quantize( y)
-ˆx clip(model.Decoder( ˆy), 0, 1)
-distortion visualLossFunction( ˆx,x)
-for0k< HLand0l<WLdo
-bitCost [k;l] min i<NCDF log2
-CPM i(ˆy[k;l] + 0:5)CPM i(ˆy[k;l]0:5)

-end for .CPM is the differentiable version of CDF
-Loss distortion+jbitCostj
-Loss.backward()
-4. Experiments
-4.1. Method
-These experiments are based on the PyTorch implemen-
-tation of Ball ´e et al. (2018) [ 6] published by Liu Jia-
-heng [ 9,13]. To implement our proposed method, the
-HP is omitted in favor of competition of expert prior dis-
-tributions. The CPM is that defined in [ 9] with an addi-
-tional NCDFdimension to compute all CDF tables in par-
-allel. Theoretical results are verified using the torchac
-range coder [ 18,17,16]. A functional training loop is de-
-scribed in Algorithm 1, and source code is provided on
-https://github.com/trougnouf/Manypriors .
-To ensure that all priors get an opportunity to train, the prior
-distributions that have not been used for at least fifty stepsare randomly assigned to spatial locations with largest bit-
-counts, to be forced to train. The Adam optimizer [ 11] is
-used with a starting learning rate (LR) of 0.0001 for the AE
-and 0.001 for the CPM. Performance is tested every 2500
-steps in inference mode on the validation set, and the LR
-is decayed by a factor of 0.99 if the performance have not
-improved for two tests. Reported performance is the one of
-the model taht minimizes (visualLoss+bitCost )on the
-validation set at the end of training. Base models are trained
-for six million steps at = 4096 with the mean squared er-
-ror (MSE) loss. Smaller values and MS-SSIM models are
-trained for four million steps starting from the base model
-with their LR and optimizer reset. All models use CH= 192
-(hidden layers channels) and CL= 256 (output channels)
-such that a single base model is needed for each prior con-
-figuration. The training and validation dataset is made of
-free-license images from Wikimedia Commons [ 3]; mainly
-“Category:Featured pictures on Wikimedia Common” which
-consists of 13928 images of the highest quality. The images
-are cropped into 10242pixels patches on disk to speed up
-further resizing, then they are resized on-the-fly by a random
-factor down to 2562pixels during training. A batch size of 4
-patches is used. The kodak set [ 2] is used as a validation set
-and the CLIC professional test dataset [ 4] is used for testing.
-The RD curve of our “multiprior” model is compared
-with that of the HP model [ 6], which is trained from scratch
-using Liu Jiaheng’s PyTorch implementation [ 9,13]. Liu
-Jiaheng’s code differs slightly from the paper’s definition [ 6]
-in that a Laplace distribution is used in place of the normal
-distribution to stabilize training. Complexity is measured as
-the number of GMac (billion multiply-accumulate operation)
-using the ptflops counter [ 25] and the number of memory
-lookup operations is calculated manually.
-4.2. Results
-The PSNR RD curve measured on the CLIC professional
-test set [ 4] is shown on top of Figure 4. The performance
-of a 64-priors model is in line with that of the HP model
-: they both perform slightly better than BPG at high bpp,
-and achieve significantly better RD than the single-prior
-model. In the middle, the RD value at = 4096 , the highest
-bitrate, is shown for 1, 2, 4, 8, 16, 32, 64, and 128 prior
-distributions. 128-priors offer marginal gains and costs an
-increased training time (1.5) and encoding time. MS-SSIM
-performance of fine-tuned models is shown in the bottom
-of Figure 4; the 64-priors model still performs similarly to
-[6], and both learned compression models benefit from this
-more perceptual metric compared with traditional codecs. A
-visual comparison of images compressed with the MSE loss
-(= 512 ) and the equivalent bitrate settings in conventional
-codecs is shown in Figure 5.
-Computational complexity of our Manypriors has been
-compared to the one of the HP model [ 6]). This complex-0.0 0.2 0.4 0.6 0.8 1.0323334353637383940PSNR 
-1-prior
-2-priors
-4-priors
-8-priors
-16-priors
-32-priors
-64-priors
-128-priors
-hyperprior
-BPG
-JPEG
-0.60 0.65 0.70 0.75 0.80 0.8538.538.638.738.838.939.039.139.2PSNR 
-1-prior
-2-priors
-4-priors
-8-priors
-16-priors
-32-priors
-64-priors
-128-priors
-hyperprior
-BPG
-JPEG
-0.0 0.2 0.4 0.6 0.8 1.0
-bpp 
-0.960.970.980.991.00MS-SSIM 
-1-prior
-64-priors
-hyperprior
-BPG
-JPEGFigure 4. Top: PSNR RD curve of a 64-priors model on the CLIC
-pro. test set, compared with the HP model [ 6], and the BPG and
-JPEG codecs. Middle : Zoom in on models with 1, 2, 4, 8, 16, 32,
-and 64 priors. Bottom: MS-SSIM RD curve.
-Table 1. Complexity of the HP model [ 6]) compared to Manypriors
-(ours), expressed in GMac for the neural network parts and number
-of memory lookup operations (* or parametrized Laplace CDF
-generations in full-precision) for the CDF tables generation, to
-process a 4K image.
-(#) Hyperprior Manypriors ratio MPHP
-EncodingGMacmain encoder 769.82 769.82
-hyper encoder 23.75
-hyper decoder 23.86
-total 817.43 769.82 0.942
-Lookupsindices 530.84 M
-CDF 829.44 K * 32.400 K
-total 829.44 K* 530.87 M N CDF= 64
-DecodingGMachyper decoder 23.154
-main decoder 769.60 769.60
-total 792.75 769.60 0.971
-Lookups CDF (total) 829.44 K* 32.400 K1
-CL= 0:004
-ity is expressed in GMac for the neural network parts and
-number of memory lookup operations. It is summarized in
-Table 1. The lack of a HP AE saves 3 % to 6 % GMac, de-
-pending on whether only the HP decoder (image decoding)
-Figure 5. Visual comparison of Larry the cat [ 1] compressed with
-learned (= 512 ) and conventional methods. Top-left: uncom-
-pressed, top-middle: JPEG (PSNR: 29.3, 0.224 bpp), top-right:
-BPG (PSNR: 32.9, 0.217 bpp), bottom-left: 1-prior (PSNR: 32.4,
-0.252 bpp), bottom-middle: hyperprior (PSNR: 32.8, 0.217 bpp),
-bottom-right: 64-priors/ours (PSNR: 32.9, 0.218 bpp)
-or the whole HP codec (image encoding) is used. Decoding
-with the Manypriors scheme is greatly simplified compared
-to [6] because the CDF tables generation process takes the
-optimal indices stored as side-information and looks up one
-static CDF table per latent spatial dimension, that is CL(typ-
-ically 256) fewer lookups than with a HP. During encoding,
-the Manypriors scheme must lookup every latent variable
-with every CDF table in order to determine the most cost
-effective CDF tables. This results in NCDF(typically 64)
-times more lookup operations than the HP scheme overall,
-although these lookup operations are relatively cheap be-
-cause only two values are needed (variable 0.5), whereas
-each CDF table lookup in [ 6] returns Lprobabilities. More-
-over, it is challenging to make an accurate CDF LUT for the
-HP scheme, because quantizing the distribution scale param-
-eter reduces the accuracy of the resulting CDFs, negatively
-impacting the bitrate. This challenge is exacerbated when
-the distribution has multiple parameters [ 19] or a mixture
-of distributions [ 7] is used. In Figure 4, LUT are replaced
-by accurate but complex Laplace distribution computation
-for the HP scheme in order to maximize the reported RD
-performance.
-Time complexity is measured for every step on CPU,
-where it can be reliably profiled due to synchroneous execu-
-tion. It is summarized in Table 2 with the following distinct
-sub-categories: NN (neural network) is the time spent in
-the AE, CDF generation is the time spent building the CDF
-tables for a specific image, and entropy is the bitstream gener-
-ation. All operations are done using the PyTorch framework
-in python, except for entropy encoding which makes use of
-the torchac range coding library [ 18,17], written in C++, andTable 2. Breaking down the image encoding and decoding time, in
-seconds. Image: 4.5 MP snail [ 1]. CPU: AMD Ryzen 7 2700X.
-Time avg. of 50 runs.
-(#)Hyperprior
-(Ball ´e2018)64-priors
-(ours)ratio
-(oursHP)
-EncodingNN encode: main + hyperprior 3.81 + 0.41 3.79 + 0.00 0.90
-entropy encode, main + hyperprior 0.15 + 0.02 0.15 + 0.00
-CDF : select indices + gather tables 0.00 +FP: 15.95
-LUT: 5.661.90 + 0.81FP: 0.17
-LUT: 0.48
-encode (total)FP: 20.33
-LUT: 10.046.65FP: 0.32
-LUT: 0.66
-DecodingNN decode : main + hyperprior 10.66 + 0.34 10.50 0.95
-CDF : gather tablesFP: 15.95
-LUT: 5.660.81FP: 0.05
-LUT: 0.14
-entropy decode : main + hyperprior 0.24 + 0.02 0.24 0.92
-decode (total)FP: 27.21
-LUT: 16.9211.54FP: 0.42
-LUT: 0.68
-the prior indices are compressed using the LZMA library [ 8].
-The total encoding time of the 64-priors model is 0.32 time
-that of the HP model and the decoding time is 0.42 times that
-of the HP model. The timing is more significant when it is
-broken down by sub-category because each component has
-a different response time depending on the hardware (and
-software) architecture in place. The AE (“NN”) encoding
-time is 0.90 that of the HP scheme and decoding time is
-0.95 time as much as the HP. Both the hyper-encoder and
-hyper-decoder are called during encoding, thus it appears
-that each part of the HP sub-network costs 5 % of the AE
-time. The time taken to build the CDF tables for the HP
-model was measured both by estimating the per-variable
-Laplace distributions (“full-precision”) and with a quantized
-scale parameter LUT. In any case, finding the best indices of
-a 64-priors model appears to be relatively inexpensive and
-the total CDF tables generation time is only 0.17 to 0.48 that
-of the HP model (depending on whether the HP model uses
-full-precision or LUT) for encoding. During decoding, the
-64-priors model spends 0.05 to 0.14 as much time building
-the CDF tables as the HP model, because the optimal CDF
-table indices have already been determined during encoding
-and they are included in the bitstream.
-5. Conclusion
-Convolutional autoencoders trained for compression are
-optimized for both rate and distortion. Rate is estimated with
-a cumulative probability model, which in turns generates a
-CDF for every latent variable to be encoded. A single CDF
-per latent channel is not sufficient to capture the statistics at
-the output of the encoder, nor to allow the encoder to express
-a wide variety of features. To support multiple statistics, the
-hyperprior [ 6] parametrizes a standard distribution, but this
-introduces a great deal of complexity in the entropy coding
-stage because the CDF differs for every latent variable to be
-encoded. The proposed method uses multiple prior distri-
-butions working as a competition of experts to capture the
-relevant features which they specialize on. This approach is
-advantageous because the learned CDF tables are stored in
-a static LUT once training is finished, and a model trainedwith 64 prior distributions performs with a similar RD as
-one trained with a HP sub-network. Moreover, a learned
-CDF table includes the CDF for all channels in the latent
-code. Hence, accessing the CDF table for a spatial location
-provides the CDF for each of its channels and the number of
-lookups is reduced to the number of latent spatial locations.
-In our experiments, CDF tables generation in the encoding
-step takes 0.17 to 0.48 as much time with a 64-priors model
-as it does with the HP model (depending on the precision
-of the HP model). This ratio is lowered to 0.05 to 0.14 dur-
-ing decoding because the prior indices have already been
-determined during the encoding.
-6. Acknowledgements
-This research has been funded by the Walloon Region.
-Computational resources have been provided by the super-
-computing facilities of the Universit ´e catholique de Lou-
-vain (CISM/UCL) and the Consortium des ´Equipements de
-Calcul Intensif en F ´ed´eration Wallonie Bruxelles (C ´ECI)
-funded by the Fond de la Recherche Scientifique de Bel-
-gique (F.R.S.-FNRS) under convention 2.5020.11 and by the
-Walloon Region.
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txt/2112.03557.txt

@@ -1,216 +0,0 @@
-arXiv:2112.03557v1  [cs.CL]  7 Dec 2021Multi-speaker Emotional Text-to-speech Synthesizer
-Sungjae Cho1,†, Soo-Young Lee2
-1Korea Institute of Science and Technology, Republic of Kore a
-2Korea Advanced Institute of Science and Technology, Republ ic of Korea
-[email protected], [email protected]
-Abstract
-We present a methodology to train our multi-speaker emotion al
-text-to-speech synthesizer that can express speech for 10 s peak-
-ers’ 7 different emotions. All silences from audio samples a re
-removed prior to learning. This results in fast learning by o ur
-model. Curriculum learning is applied to train our model effi -
-ciently. Our model is first trained with a large single-speak er
-neutral dataset, and then trained with neutral speech from a ll
-speakers. Finally, our model is trained using datasets of em o-
-tional speech from all speakers. In each stage, training sam ples
-of each speaker-emotion pair have equal probability to appe ar
-in mini-batches. Through this procedure, our model can synt he-
-size speech for all targeted speakers and emotions. Our synt he-
-sized audio sets are available on our web page.
-Index Terms : emotional speech synthesis, text-to-speech, ma-
-chine learning, neural network, deep learning
-1. Introduction
-Emotional speech synthesis has been achieved through deep
-neural networks [1, 2, 3, 4]. However, most studies have trai ned
-models on a small number of speakers or balanced class distri -
-butions because it is challenging to guarantee speech quali ty for
-each speaker and emotion, given imbalanced data distributi ons
-with respect to speakers and emotions. In this paper, we pres ent
-a methodology for training our multi-speaker emotional tex t-to-
-speech (TTS) synthesizer capable of generating speech for a ll
-targeted speakers’ voices and emotions. The main methods ar e
-silence removal, curriculum learning [5], and oversamplin g [6].
-The synthesized audios are demonstrated through a web page.
-2. Datasets
-4 datasets were used to train the multi-speaker emotional TT S
-synthesizer. The first dataset, the Korean single speaker sp eech
-(KSS) dataset [7], is publicly available and contains speec h
-samples of a single female speaker: kss-f. We labeled their
-emotion as neutral. The remaining 3 datasets consist of spee ch
-of the Ekman’s 7 basic emotions [8]: neutral, anger, disgust ,
-fear, happiness, sadness, and surprise.
-The first Korean emotional TTS (KETTS) dataset consists
-of 1 female and 1 male speaker: ketts-30f and ketts-30m, whic h
-are abbreviations of a 30’s female and male in KETTS. The 2
-speakers were assigned to different sets of sentences; howe ver,
-the same sentences were recorded across 7 emotions for a sing le
-speaker. In the female case, only happy speech samples have a
-different set of sentences. KETTS is balanced with respect t o
-speakers and emotions, except for the female’s happy speech
-subset (Table 1).
-The second Korean emotional TTS (KETTS2) dataset con-
-sists of 3 female and 3 male speakers, totally 6 speakers: ket t2-
-†work done at KAISTTable 1: Hours of preprocessed training datasets
-Speaker all neu ang dis fea hap sad sur
-kss-f 12.59 12.59
-ketts-30f 26.61 3.52 3.46 3.51 3.68 5.13 3.75 3.56
-ketts-30m 24.12 3.37 3.29 3.31 3.51 3.50 3.73 3.40
-ketts2-20m 5.09 0.72 0.72 0.74 0.76 0.69 0.75 0.70
-ketts2-30f 4.69 0.66 0.65 0.67 0.65 0.70 0.68 0.68
-ketts2-40m 4.98 0.73 0.69 0.70 0.75 0.69 0.74 0.69
-ketts2-50f 4.98 0.73 0.71 0.71 0.70 0.72 0.71 0.69
-ketts2-50m 4.73 0.68 0.68 0.69 0.67 0.68 0.68 0.65
-ketts2-60f 4.90 0.77 0.68 0.67 0.68 0.72 0.72 0.67
-ketts3-f 9.64 3.96 1.34 1.27 1.44 1.64
-ketts3-m 9.38 3.90 1.43 1.18 1.39 1.48
-all 111.70 31.63 13.65 11.01 13.85 15.64 14.87 11.05
-20m, ketts2-30f, ketts2-40m, ketts-50f, ketts2-50m, and k etts2-
-60f. The same sentences were recorded across 7 emotions and 6
-speakers. Hence, KETTS2 is balanced with respect to speaker s
-and emotions (Table 1).
-The third Korean emotional TTS (KETTS3) dataset con-
-sists of 1 female and 1 male speaker: ketts3-f and ketts3-m. I t
-includes 5 emotions, excluding disgust and surprise. The sa me
-sentences were recorded across 2 speakers; however, differ ent
-sentences were spoken for the 5 emotions. KETTS3 is balanced
-for speakers but not for emotions. Therefore, the whole trai ning
-dataset is balanced for neither speakers nor emotions (Tabl e 1).
-3. Methodology
-3.1. Preprocessing
-The WebRTC voice activity detector, py-webrtcvad1, is utilized
-to remove unvoiced segments in audios, with its settings of a n
-aggressiveness level of 3, frame duration 30ms, and padding
-duration 150ms. These settings remove silences at the start , end,
-and middle of speech. However, the amount of silence removed
-does not distort emotional expression. All audios are resam pled
-to sampling rate 22,050Hz. Mel spectrograms are computed
-through a short-time Fourier transform (STFT) using frame s ize
-1024, hop size 256, window size 1024, and a Hann window
-function. The STFT magnitudes are transformed to the libros a
-Slaney mel scale using an 80-channel mel filterbank spanning
-0Hz to 8kHz, and the results are then clipped to a minimum
-value of10−5, followed by log dynamic range compression.
-Every Korean character in an input sentence is decomposed
-into 3 elements: an onset, nucleus, and coda. In total, 19 ons ets,
-21 nuclei, and 28 codas including the empty coda are employed
-as defined by Unicode. A sequence of these elements becomes
-a grapheme sequence taken as input by our synthesizer.
-1https://github.com/wiseman/py-webrtcvad3.2. Model
-Our multi-speaker emotional TTS synthesizer takes 3 inputs —
-the grapheme sequence of a Korean sentence, 1 of 10 speakers
-(5 females, 5 males), and 1 of the 7 Ekman’s emotion classes. I t
-then generates a waveform in which the speaker utters the inp ut
-sentence with the given emotion. Our synthesizer consists o f 2
-sub-models: Tacotron 2 [9], mapping a grapheme sequence to
-a mel spectrogram, and WaveGlow [10], transforming the mel
-spectrogram to a waveform. Tacotron 2 is an auto-regressive
-sequence-to-sequence neural network with a location-sens itive
-attention mechanism. WaveGlow is a flow-based generative
-neural network without auto-regression. We adapted NVIDIA
-Tacotron 2 and WaveGlow repositories2,3to synthesize speech
-for multiple speakers and emotions. The WaveGlow model
-was utilized without modification but the Tacotron 2 model wa s
-modified as outlined in the following paragraph.
-Speaker identity is represented as a 5-dimensional train-
-able speaker vector . Emotion identity is represented as a 3-
-dimensional trainable emotion vector , except for the neutral
-emotion vector, which is a non-trainable zero vector. To syn -
-thesize speech of a given speaker and emotion, in the decoder
-of Tacotron 2, speaker and emotion vectors are concatenated to
-attention context vectors taken by the first and second LSTM
-layers and the linear layer estimating a mel spectrogram.
-3.3. Training
-Tacotron 2 was trained with a batch size of 64 equally dis-
-tributed to 4 GPUs. The Adam optimizer [11] of the default
-settings (β1= 0.9,β2= 0.999,ǫ= 10−6) was used with a
-learning rate of 10−3andL2regularization with weight 10−6.
-If the norm of gradients exceeded 1, their norm was normalize d
-to 1 to ensure stable learning.
-Using a curriculum learning [5] strategy, Tacotron 2 was
-trained to learn single-speaker neutral speech, multi-spe aker
-neutral speech, and multi-speaker emotional speech in this or-
-der. More specifically, the model was trained with the KSS
-dataset for 20,000 iterations, then additionally with all d atasets
-of neutral speech for 30,000 iterations, and finally with all train-
-ing datasets for 65,000 iterations. Transitioning to train ing on
-the next dataset was done when the model stably pronounced
-given whole sentences for all training speaker-emotion pai rs.
-In each training stage, we oversampled [6] the training set
-with respect to speaker-emotion pairs, which means samples of
-each speaker-emotion pair appear in a mini-batch with equal
-probability. For example, samples of (ketts-30f, neutral) and
-those of (ketts2-20m, happy) appear in a mini-batch with equ al
-probability. This helped overcome difficulty in learning to syn-
-thesize speech of speaker-emotion pairs with relatively sc arce
-samples.
-WaveGlow was trained with a batch size of 24, equally dis-
-tributed to 3 GPUs using 24 clips of 16,000 mel spectrogram
-frames randomly chosen from each training sample. Training
-samples shorter than 16,000 mel frames were excluded from th e
-training set since these samples padded with zeros caused un sta-
-ble learning such as exploding gradients. Similar to Tacotr on 2,
-we oversampled the training set with respect to speaker-emo tion
-pairs. The Adam optimizer was used with the default settings
-and learning rate 10−4. Weight normalization was applied, as
-described in the original paper [10]. To ensure stable learn ing,
-if the norm of gradients exceeded 1, their norm was normalize d
-2https://github.com/NVIDIA/tacotron2
-3https://github.com/NVIDIA/waveglowto 1. The model was initialized with the pretrained weights4of-
-fered in the WaveGlow repository. The network was trained fo r
-400,000 iterations until its loss curve formed a plateau. Th ez
-elements were sampled from Gaussians with standard deviati on
-1 during training and 0.75 during inference.
-4. Results and Discussion
-Through this procedure, our speech synthesizer is able to sy n-
-thesize speech for all available 10 speakers and 7 emotions. Un-
-expectedly, disgusted and surprised expressions of the KET TS3
-speakers can be synthesized even without training supervis ion.
-Synthesized speech samples can be found on this web page5.
-Although our model expresses speaker and emotion iden-
-tities, there are some minor inconsistencies in the quality of
-synthesized samples across speakers and emotions. Thus, in
-production, it is reasonable to fine-tune for each speaker an d
-respectively preserve the model parameters.
-Our silence removal settings substantially accelerated th e
-learning of Tacotron 2. This was probably because silence re -
-moval at the start, end, and middle of speech resulted in the
-linear relationship between text and speech, and this relat ion-
-ship helped the location-sensitive attention network easi ly learn
-text-to-speech alignments.
-5. Acknowledgements
-This work was supported by Ministry of Culture, Sports and
-Tourism andKorea Creative Content Agency [R2019020013,
-R2020040298].
-6. References
-[1] Y . Lee, A. Rabiee, and S.-Y . Lee, “Emotional end-to-end n eural
-speech synthesizer,” ArXiv , vol. abs/1711.05447, 2017.
-[2] H. Choi, S. Park, J. Park, and M. Hahn, “Multi-speaker emo tional
-acoustic modeling for CNN-based speech synthesis,” in ICASSP ,
-2019.
-[3] S.-Y . Um, S. Oh, K. Byun, I. Jang, C. Ahn, and H.-G. Kang,
-“Emotional speech synthesis with rich and granularized con trol,”
-inICASSP , 2020.
-[4] T.-H. Kim, S. Cho, S. Choi, S. Park, and S.-Y . Lee, “Emotio nal
-voice conversion using multitask learning with text-to-sp eech,” in
-ICASSP , 2020.
-[5] Y . Bengio, J. Louradour, R. Collobert, and J. Weston, “Cu rriculum
-learning,” in ICML , 2009.
-[6] M. Buda, A. Maki, and M. A. Mazurowski, “A systematic stud y
-of the class imbalance problem in convolutional neural netw orks,”
-Neural Networks , vol. 106, 2018.
-[7] K. Park, “KSS dataset: Korean single speaker speech data set,”
-https://kaggle.com/bryanpark/korean-single-speaker- speech-dataset,
-2018.
-[8] P. Ekman and D. Cordaro, “What is meant by calling emotion s
-basic,” Emotion Review , vol. 3, no. 4, 2011.
-[9] J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Ya ng,
-Z. Chen, Y . Zhang, Y . Wang, R.-S. Ryan, R. A. Saurous,
-Y . Agiomyrgiannakis, and Y . Wu, “Natural TTS synthesis by co n-
-ditioning wavenet on MEL spectrogram predictions,” in ICASSP ,
-2018.
-[10] R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow -based
-generative network for speech synthesis,” in ICASSP , 2019.
-[11] D. P. Kingma and J. Ba, “Adam: A method for stochastic opt i-
-mization,” in ICLR , 2015.
-4“waveglow_256channels_universal_v5.pt” was used.
-5https://sungjae-cho.github.io/InterSpeech2021_STDem o/

txt/2201.00132.txt

@@ -1,617 +0,0 @@
-SAFL: A Self-Attention Scene Text Recognizer
-with Focal Loss
-Bao Hieu Tran, Thanh Le-Cong, Huu Manh Nguyen, Duc Anh Le, Thanh Hung Nguyeny, Phi Le Nguyeny
-School of Information and Communication Technology
-Hanoi University of Science and Technology
-Hanoi, Vietnam
-fhieu.tb167182, thanh.ld164834, manh.nh166428, anh.nd160126 [email protected],flenp, [email protected]
-Abstract —In the last decades, scene text recognition has gained
-worldwide attention from both the academic community and
-actual users due to its importance in a wide range of applications.
-Despite achievements in optical character recognition, scene
-text recognition remains challenging due to inherent problems
-such as distortions or irregular layout. Most of the existing
-approaches mainly leverage recurrence or convolution-based
-neural networks. However, while recurrent neural networks
-(RNNs) usually suffer from slow training speed due to sequential
-computation and encounter problems as vanishing gradient or
-bottleneck, CNN endures a trade-off between complexity and
-performance. In this paper, we introduce SAFL, a self-attention-
-based neural network model with the focal loss for scene text
-recognition, to overcome the limitation of the existing approaches.
-The use of focal loss instead of negative log-likelihood helps the
-model focus more on low-frequency samples training. Moreover,
-to deal with the distortions and irregular texts, we exploit Spatial
-TransformerNetwork (STN) to rectify text before passing to the
-recognition network. We perform experiments to compare the
-performance of the proposed model with seven benchmarks.
-The numerical results show that our model achieves the best
-performance.
-Index Terms —Scene Text Recognition, Self-attention, Focal
-loss,
-I. I NTRODUCTION
-In recent years, text recognition has attracted the attention
-of both academia and actual users due to its application
-on various domains such as translation in mixed reality,
-autonomous driving, or assistive technology for the blind.
-Text recognition can be classified into two main categories:
-scanned document recognition and scene text recognition.
-While the former has achieved significant advancements, the
-latter remains challenging due to scene texts’ inherent char-
-acteristics such as the distortion and irregular shapes of the
-texts. Recent methods in scene text recognition are inspired
-by the success of deep learning-based recognition models.
-Generally, these methods can be classified in two approaches:
-recurrent neural networks (RNN) based and convolutional
-neural networks (CNN) based. RNN-based models have shown
-their effectiveness, thanks to capturing contextual information
-and dependencies between different patches. However, RNNs
-typically compute along with the symbol positions of the
-input and output sequences, which cannot be performed in
-Authors contribute equallyyCorresponding authorparallel fashion, thus leads to high training time. Furthermore,
-RNNs also encounter problems such as vanishing gradient
-[1] or bottleneck [2]. CNN-based approach, which allows
-computing the hidden representation parallelly, have been
-proposed to speed up the training procedure. However, to
-capture the dependencies between distant patches in long input
-sequences, CNN models require stacking more convolutional
-layers, which significantly increases the network’s complexity.
-Therefore, CNN-based methods suffer the trade-off between
-complexity and accuracy. To remedy these limitations, in nat-
-ural language processing (NLP) fields, a self-attention based
-mechanism named transformer [3] has been proposed. In the
-transformer, dependencies between different input and output
-positions are captured using a self-attention mechanism instead
-of sequential procedures in RNN. This mechanism allows
-more computation parallelization with higher performance. In
-the computer vision domain, some research have leveraged the
-transformer architecture and showed the effectiveness of some
-problems [4] [5]
-Inspired by the transformer network, in this paper, we
-propose a self-attention based scene text recognizer with focal
-loss, namely as SAFL. Moreover, to tackle irregular shapes of
-scene texts, we also exploit a text rectification named Spatial
-Transformer Network (STN) to enhance the quality of text
-before passing to the recognition network. SAFL, as depicted
-in Figure 1, contains three components: rectification, feature
-extraction, and recognition. First, given an input image, the
-rectification network, built based on the Spatial Transformer
-Network (STN) [6], transforms the image to rectify its text.
-Then, the features of the rectified image are extracted using
-a convolutional neural network. Finally, a self-attention based
-recognition network is applied to predict the output character
-sequence. Specifically, the recognition network is an encoder-
-decoder model, where the encoder utilizes multi-head self-
-attention to transform input sequence to hidden feature rep-
-resentation, then the decoder applies another multi-head self-
-attention to output character sequence. To balance the training
-data for improving the prediction accuracy, we exploit focal
-loss instead of negative log-likelihood as in most recent works
-[7] [8].
-To evaluate our proposed model’s performance, we train
-SAFL with two synthetic datasets: Synth90k [9] and SynthText
-[10], and compare its accuracy with standard benchmarks,arXiv:2201.00132v1  [cs.CV]  1 Jan 2022Fig. 1. Overview of SAFL
-on both regular and irregular datasets. The experiment results
-show that our method outperforms the state-of-the-art on all
-datasets. Furthermore, we also perform experiments to study
-the effectiveness of focal loss. The numerical results show the
-superiority of focal loss over the negative log-likelihood loss
-on all datasets.
-The remainder of the paper is organized as follows. Section
-II introduces related works. We describe the details of the
-proposed model in Section III and present the evaluation
-results in Section IV. Finally, we conclude the paper and
-discuss the future works in Section V.
-II. R ELATED WORK
-Scene text recognition has attracted great interest over
-the past few years. Comprehensive surveys for scene text
-recognition may be found in [11] [12] [13]. As categorized
-by previous works [8] [14] [15], scene text may be divided
-into two categories: regular and irregular text. The regular text
-usually has a nearly horizontal shape, while the irregular text
-has an arbitrary shape, which may be distorted.
-A. Regular text recognition
-Early work mainly focused on regular text and used a
-bottom-up scheme, which first detects individual characters
-using a sliding window, then recognizing the characters us-
-ing dynamic programming or lexicon search [16] [17] [18].
-However, these methods have an inherent limitation, which is
-ignoring contextual dependencies between characters. Shi et al.
-[19] and He et al. [20] typically regard text recognition as a
-sequence-to-sequence problem. Input images and output texts
-are typically represented as patch sequences and character
-sequences, respectively. This technique allows leveraging deep
-learning techniques such as RNNs or CNNs to capture con-
-textual dependencies between characters [7] [19] [20], lead to
-significant improvements in accuracy on standard benchmarks.
-Therefore, recent work has shifted focus to the irregular text,
-a more challenging problem of scene text recognition.
-B. Irregular text recognition
-Irregular text is a recent challenging problem of scene text
-recognition, which refers to texts with perspective distortionsand arbitrary shape. The early works correct perspective distor-
-tions by using hand-craft features. However, these approaches
-require correct tunning by expert knowledge for achieving
-the best results because of a large variety of hyperparame-
-ters. Recently, Yang et al. [21] proposed an auxiliary dense
-character detection model and an alignment loss to effectively
-solve irregular text problems. Liu et al. [22] introduced a
-Character-Aware Neural Network (Char-Net) to detect and rec-
-tify individual characters. Shi et al. [7] [8] addressed irregular
-text problems with a rectification network based on Spatial
-Transformer Network (STN), which transform input image for
-better recognition. Zhan et al. [23] proposed a rectification
-network employing a novel line-fitting transformation and an
-iterative rectification pipeline for correction of perspective and
-curvature distortions of irregular texts.
-III. P ROPOSED MODEL
-Figure 1 shows the structure of SAFL, which is comprised
-of three main components: rectification, feature extraction, and
-recognition. The rectification module is a Spatial Transformer
-Network (STN) [6], which receives the original image and
-rectifies the text to enhance the quality. The feature extraction
-module is a convolution neural network that extracts the
-information of the rectified image and represents it into a
-vector sequence. The final module, i.e., recognition, is based
-on the self-attention mechanism and the transformer network
-architecture [3], to predict character sequence from the feature
-sequence. In the following, we first present the details of the
-three components in Section III-A, III-B and III-C, respec-
-tively. Then, we describe the training strategy using focal loss
-in Section III-D.
-A. Rectification
-In this module, we leverage a Thin Plate Spline (TPS) trans-
-formation [8], a variant of STN, to construct a rectification
-network. Given the input image Iwith an arbitrary size, the
-rectification module first resizes Iinto a predefined fixed size.
-Then the module detects several control points along the top
-and bottom of the text’s bounding. Finally, TPS applies asmooth spline interpolation between a set of control points
-to rectify the predicted region to obtain a fixed-size image.
-B. Feature Extraction
-We exploit the convolution neural network (CNN) to extract
-the features of the rectified image (obtained from a rectification
-network) into a sequence of vectors. Specifically, the input
-image is passed through convolution layers (ConvNet) to
-produce a feature map. Then, the model separates the feature
-map by rows. The output received after separating the feature
-map are feature vectors arranged in sequences. The scene
-text recognition problem then becomes a sequence-to-sequence
-problem whose input is a sequence of characteristic vectors,
-and whose output is a sequence of characters predicted. Based
-on the proposal in [3], we further improve information about
-the position of the text in the input image by using positional
-encoding. Each position posis represented by a vector whose
-value of the ithdimension, i.e., PE (pos;i ), is defined as
-PE (pos;i )=8
-<
-:sinpos
-100002i
-dmodel;if0idmodel
-2
-cospos
-100002i
-dmodel;ifdmodel
-2idmodel;
-(1)
-wheredmodel is the vector size. The position information is
-added into the encoding vectors.
-C. Self-attention based recognition network
-The architecture of the recognition network follows the
-encoder-decoder model. Both encoder blocks and decoder
-blocks are built based on the self-attention mechanism. We
-will briefly review this mechanism before describing each
-network’s details.
-1) Self-attention mechanism: Self-attention is a mechanism
-that extracts the correlation between different positions of a
-single sequence to compute a representation of the sequence.
-In this paper, we utilize the scaled dot-product attention
-proposed in [3]. This mechanism consists of queries and keys
-of dimension dk, and values of dimension dv. Each query
-performs the dot product of all keys to obtain their correlation.
-Then, we obtain the weights on the values by using the softmax
-function. In practice, the keys, values, and queries are also
-packed together into matrices K,VandQ. The matrix of the
-outputs is computed as follow:
-Attention (Q;K;V ) =softmaxQKT
-pdk
-V (2)
-The dot product is scaled by1pdkto alleviate the small softmax
-values which lead to extremely small gradients with large
-values ofdk. [3].
-2) Encoder: Encoder is a stack of Neblocks. Each block
-consists of two main layers. The first layer is a multi-head
-attention layer, and the second layer is a fully-connected feed-
-forward layer. The multi-head attention layer is the combi-
-nation of multiple outputs of the scale dot product attention.
-Each scale-dot product attention returns a matrix representing
-the feature sequences, which is called head attention. The
-combination of multiple head attentions to the multi-head
-Fig. 2. Frenquency of characters in training lexicon
-attention allows our model to learn more representations of
-feature sequences, thereby increasing the diversity of the
-extracted information, and thereby enhance the performance.
-Multi-head attention can be formulated as follows:
-MultiHead (Q;K;V ) =Concat (head 1;:::;head h)WO
-(3)
-whereheadi=Attention
-QWQ
-i;KWK
-i;VWV
-i
-,his the
-number of heads, WQ
-i2Rdmodeldk;WK
-i2Rdmodeldk;WV
-i2
-Rdmodeldv,WO2Rhdvdmodel are weight matrices. dk;dv
-anddmodel are set to the same value. Layer normalization
-[24] and residual connection [25] are added into each main
-layer (i.e., multi-head attention layer and fully-connected
-layer) to improve the training effect. Specifically, the residual
-connections helps to decrease the loss of information in the
-backpropagation process, while the normalization makes the
-training process more stable. Consequently, the output of
-each main layer with the input xcan be represented as
-LayerNorm (x+Layer (x)), whereLayer (x)is the function
-implemented by the layer itself, and LayerNorm ()represents
-the normalization operation. The blocks of the encoder are
-stacked sequentially, i.e., the output of the previous block is
-the input of the following block.
-3) Decoder: The decoding process predicts the words in
-a sentence from left to right, starting with the hstartitag
-until encountering the henditag. The decoder is comprised of
-Nddecoder blocks. Each block is also built based on multi-
-head attention and a fully connected layer. The multi-head
-attention in the decoder does not consider words that have
-not been predicted by weighting these positions with 1.
-Furthermore, the decoder uses additional multi-head attention
-that receives keys and values from the encoder and queries
-from the decoder. Finally, the decoder’s output is converted
-into a probability distribution through a linear transformation
-and softmax function.
-D. Training
-Figure 2 shows that the lexicon of training datasets suffers
-from an unbalanced sample distribution. The unbalance may
-lead to severe overfitting for high-frequency samples and un-
-derfitting for low-frequency samples. To this end, we propose
-to use focal loss [26] instead of negative log-likelihood as in
-most of recent methods [7] [8]. By exploiting focal loss, themodel will not encounter the phenomenon of ignoring to train
-low-frequency samples.
-Focal loss is known as an effective loss function to address
-the unbalance of datasets. By reshaping the standard cross-
-entropy loss, focal loss reduces the impacts of high-frequency
-samples and thus focus training on low-frequency ones [26].
-The focal loss is defined as follows:
-FL(pt) =t(1pt)
-where,ptis the probability of the predicted value, computed
-using softmax function, and
-used to balance the loss. Intuitively, focal loss is obtained
-by multiplying cross entropy by t(1pt)
-weightt(1pt)
-the focal loss helps to reduce the impact of high-frequency
-samples (whose value of ptis usually high) and focus more
-on low-frequency ones (which usually have low value of pt).
-Based on focal loss, we define our training objective as
-follows:
-L=TX
-t=1(t(1p(ytjI))
-whereytare the predicted characters, Tis the length of the
-predicted sequence, and Iis the input image.
-IV. P ERFORMANCE EVALUATION
-In this section, we conduct experiments to demonstrate the
-effectiveness of our proposed model. We first briefly introduce
-datasets used for training and testing, then we describe our
-implementation details. Next, we analyze the effect of focal
-loss on our model. Finally, we compare our model against
-state-of-the-art techniques on seven public benchmark datasets,
-including regular and irregular text.
-A. Datasets
-The training datasets contains two datasets: Synth90k and
-SynthText . Synth90k is a synthetic dataset introduced in [9].
-This dataset contains 9 million images created by combining
-90.000 common English words and random variations and
-effects. SynthText is a synthetic dataset introduced in [10],
-which contains 7 million samples by the same generation
-process as Synth90k [9]. However, SynthText is targeted for
-text detection so that an image may contain several words. All
-experiments are evaluated on seven well-known public bench-
-marks described, which can be divided into two categories:
-regular text and irregular text. Regular text datasets include
-IIIT5K, SVT, ICDAR03, ICDAR13.
-IIIT5K [27] contains 3000 test images collected from
-Google image searches.
-ICDAR03 [28] contains 860 word-box cropped images.
-ICDAR13 [29] contains 1015 word-box cropped images.
-SVT contains 647 testing word-box collected from
-Google Street View.
-Irregular text datasets include ICDAR15, SVT-P, CUTE.ICDAR15 [30] contains 1811 testing word-box cropped
-images collected from Google Glass without careful
-positioning and focusing.
-SVT-P [31] contains 645 testing word-box cropped im-
-ages collected from Google Street View. Most of them
-are heavily distorted by the non-frontal view angle.
-CUTE [32] contains 288 word-box cropped images,
-which are curved text images.
-B. Configurations
-1) Implementation Detail: We implement the proposed
-model by Pytorch library and Python programming language.
-The model is trained and tested on an NDIVIA RTX 2080 Ti
-GPU with 12GB memory. We train the model from scratch
-using Adam optimizer with the learning rate of 0:00002 .
-To evaluate the trained model, we use dataset III5K. The
-pretrained model and code are available at [33]
-2) Rectification Network: All input images are resized
-to64256 before applying the rectification network. The
-rectification network consists of three components: a localiza-
-tion network, a thin plate spline (TPS) transformation, and a
-sampler. The localization network consists of 6 convolutional
-layers with the kernel size of 33and two fully-connected
-(FCN) layers. Each FCN is followed by a 22max-pooling
-layer. The number of the output filters is 32, 64, 128, 256,
-and 256. The number of output units of FCN is 512 and 2K,
-respectively, where Kis the number of the control points. In
-all experiments, we set Kto 20, as suggested by [8]. The
-sampler generates the rectified image with a size of 32100.
-The size of the rectified image is also the input size of the
-feature extraction module.
-3) Feature Extraction: We construct the feature extraction
-module based on Resnet architecture [25]. The configurations
-of the feature extraction network are listed in Table I. Our
-feature extraction network contains five blocks of 45 residual
-layers. Each residual unit consists of a 11convolutional
-layer, followed by a 33convolution layer. In the first
-two blocks, we use 22stride to reduce the feature map
-dimension. In the next blocks, we use 21stride to down-
-sampled feature maps. The 21stride also allows us to
-retain more information horizontally to distinguish neighbor
-characters effectively.
-TABLE I
-FEATURE EXTRACTION NETWORK CONFIGURATIONS .EACH BLOCK IS A
-RESIDUAL NETWORK BLOCK . ”S”STANDS FOR STRIDE OF THE FIRST
-CONVOLUTIONAL LAYER IN A BLOCK .
-Layer Feature map size Configuration
-EncoderBlock 0 32100 33conv, s( 11)
-Block 1 1650
-11 conv ;32
-33 conv ;32
-3, s(22)
-Block 2 825
-11 conv ;64
-33 conv ;32
-3, s(22)
-Block 3 425
-11 conv ;128
-33 conv ;32
-3, s(21)
-Block 4 225
-11 conv ;256
-33 conv ;32
-3, s(21)
-Block 5 125
-11 conv ;512
-33 conv ;32
-3, s(21)4) Recognition: The number of blocks in the encoder and
-the decoder are set both to 4. In each block of the encoder and
-the decoder, the dimension of the feed forward vector and the
-ouput vector are set to 2048 and512, respectively. The number
-of head attention layers is set to 8. The decoder recognizes 94
-different characters, including numbers, alphabet characters,
-uppercase, lowercase, and 32 punctuation in ASCII.
-C. Result and Discussion
-1) Impact of focal loss: To analyze the effect of focal loss,
-we study two variants of the proposed model. The first variant
-uses negative log-likelihood, and the second one leverages
-focal loss.
-TABLE II
-RECOGNITION ACCURACIES WITH NEGATIVE LOG -LIKELIHOOD AND
-FOCAL LOSS
-Variant Negative log-likelihood Focal Loss
-IIIT5K 92.6 93.9
-SVT 85.8 88.6
-ICDAR03 94.1 95
-ICDAR13 92 92.8
-ICDAR15 76.1 77.5
-SVT-P 79.4 81.7
-CUTE 80.6 85.4
-Avarage 86.9 88.2
-As shown in Table II, the model with focal loss outperforms
-the one with log-likelihood on all datasets. Notably, on aver-
-age, focal loss improves the accuracy by 2.3 % compared to
-log-likelihood. For the best case, i.e., CUTE, the performance
-gap between the two variants is 4.8 %
-2) Impact of rectification network: In this section, we study
-the effect of text rectification by comparing SAFL and a
-variant which does not include the rectification module.
-TABLE III
-RECOGNITION ACCURACIES WITH AND WITHOUT RECTIFICATION
-Variant SAFL w/o text rectification SAFL
-IIIT5K 90.7 93.9
-SVT 83.3 88.6
-ICDAR03 93 95
-ICDAR13 90.7 92.8
-ICDAR15 72.9 77.5
-SVT-P 71.6 81.7
-CUTE 77.4 85.4
-Avarage 84.1 88.2
-Table III depicts the recognition accuracies of the two mod-
-els over seven datasets. It can be observed that the rectification
-module increases the accuracy significantly. Specifically, the
-performance gap between SAFL and the one without the
-rectification module is 4:1%on average. In the best cases,
-SAFL improves the accuracy by 10:1%and7%compared
-to the other on the datasets SVT-P and CUTE, respectively.
-The reason is that both SVT-P and CUTE contains many both
-irregular texts such as perspective texts or curved texts.
-3) Comparison with State-of-the-art: In this section, we
-compare the performance of SAFL with the latest approaches
-in scene text recognition. The evaluation results are shownin Table IV. In each column, the best value is bolded.
-the ”Avarage” column is the weighted average over all the
-data sets. Concerning the irregular text, it can be observed
-that SAFL achieves the best performance on 3data sets.
-Particularly, SAFL outperforms the current state-of-the-art,
-ESIR [23], by a margin of 1:2% on average, particulary
-on CUTE ( +2:1%) and SVT-P ( +2:1%). Concerning the
-regular datasets, SAFL outperforms the other methods on two
-datasets IIIT5K and ICDAR03. Moreover, SAFL also shows
-the highest average accuracy over all the regular text datasets.
-To summarize, SAFL achieves the best performance on 5 of
-7 datasets and the highest average accuracy on both irregular
-and regular texts.
-V. C ONCLUSION
-In this paper, we proposed SAFL, a deep learning model
-for scene text recognition, which exploits self-attention mecha-
-nism and focal loss. The experiment results showed that SAFL
-achieves the highest average accuracy on both the regular
-datasets and irregular datasets. Moreover, SAFL outperforms
-the state-of-the-art on CUTE dataset by a margin of 2:1%.
-Summary, SAFL shows superior performance on 5 out of 7
-benchmarks, including IIIT5k, ICDAR 2003, ICDAR 2015,
-SVT-P and CUTE.
-ACKNOWLEDGMENT
-We would like to thank AIMENEXT Co., Ltd. for support-
-ing our research.
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txt/2201.01661.txt

@@ -1,1600 +0,0 @@
-1 
- 
-  
-Evaluation of Thermal  Imaging on Embedded GPU 
-Platforms  for Application in Vehicular Assistance 
-Systems    
- 
-Muhammad Ali Farooq, Waseem Shariff, Peter C orcoran, Fellow, IEEE   
-Abstract—This study is focused on evaluating the real-time 
-performance of thermal object detection for smart and safe 
-vehicular systems by deploying the trained networks on GPU & 
-single -board EDGE -GPU computing platforms for onboard 
-automotive sensor suite testing. A novel large -scale thermal 
-dataset comprising of > 35,000 distinct frames is acquired, 
-processed, and open -sourced  in challenging weather and 
-environmental scenarios . The dataset is a recorded  from lost -cost 
-yet effective uncooled LWIR thermal camera , mounted stand -
-alone and on an electric vehicle to minimize mechanical vibrations . 
-State -of-the-art YOLO -V5 networks variants are trained using 
-four different public datasets as well newly acquired local dataset 
-for optimal generalization of DNN by employing SGD optimizer.  
-The effectiveness of trained networks is validated on extensive test 
-data u sing various quantitative metrics which include precision, 
-recall curve, mean average precision, and frames per second. T he 
-smaller network  variant  of YOLO is further optimized using 
-TensorRT inference accelerator  to explicitly boost the frames per 
-second rate. Optimized network engine increases the frames per 
-second rate by 3.5 times when testing on low power edge devices  
-thus achieving 11  fps on Nvidia Jetson Nano and 60 fps on Nvidia 
-Xavier NX development boards . 
- 
-Index Terms — ADAS, Object detection, Thermal imaging, 
-LWIR, CNN, Edge computing  
-I. INTRODUCTION  
-hermal imaging is the digital interpretation of the 
-infrared radiations emitted from the object. Thermal 
-imaging cameras with microbolometer focal plane 
-arrays (FPA) is a type of uncooled detector that provides low -
-cost solutions for acquiring thermal images in different weather 
-and environmental conditions. These cameras when integrated 
-with AI -based imaging pipelines can be used for various real-
-world  applications.  In this work, the core focus is to design an 
-intelligent thermal object detection -based video analysis system 
-for automotive sensor suite application that should be effective 
-in all light conditions thus enabling safe and more reli able road 
-journeys. Unlike other video solutions such as visible imaging 
-which mainly relies on reflected light thus having the greater 
-chances of being blocked by visual impediments, thermal 
-imaging does not require any external lighting conditions to 
-capture quality images and it can see through visual obscurants 
- 
-October 25th, 2021 , “This research work is funded by  the ECSEL Joint 
-Undertaking (JU) under grant agreement No 826131  (Heliaus project  
-https://www.heliaus.eu/  ). The JU receives support from the European Union’s 
-Horizon 2020 research and  innovation program and National funders  from 
-France, Germany, Ireland (Enterprise Ireland , International Research Fund), 
-and Italy ”.   such as dust, light fog, smoke, or other such occlusions. 
-Moreover, the integration of AI-based thermal imaging systems 
-can provide us with a multitude of advantages from better 
-analytics with fe wer false alarms to increased coverage , provide  
-redundancy  and, higher return on investment.  
-In th is research work , we have focused on utilizing thermal 
-data for designing efficient AI -based object detection and 
-classification pipeline for Advance Driver -Assistance Systems.  
-Such type of thermal imaging -based forward sensing (F -sense) 
-system  is useful in providing enhance d safety and security 
-feature s thus enabling the driver to better scrutinize the 
-complete road -side environment.  For this purpose, we ha ve 
-used a state-of-the-art (SoA)  end-to-end deep learning 
-framework YOLO -V5 on thermal data. In the first phase , a 
-novel thermal data set is acquired for training and validation 
-purposes of different network variants of YOLO -V5. The data 
-is captured using a  prototype low -cost uncooled LWIR thermal 
-camera specifically designed under the ECSEL Heliaus  
-research project [ 32]. The raw thermal data is processed using 
-shutterless camera calibration, automatic gain control, bad -
-pixel removal , and temporal denoising methods.  
-Furthermore,  the trained network variants are deployed and 
-tested on two state-of-the-art embedded GPU platforms,  which 
-include NVIDIA  Jetson nano  [23] and Nvidia Jetson Xavier  NX 
-[25]. Thus, studying the extensive real -time and on -board 
-feasibility in terms of various quantitative metrics, inference 
-time, FPS,  and hardware sensor temperatures.  
-The core contributions  of the proposed research work are 
-summarized below : 
-• Preparation and annotation of a large o pen-access dataset of 
-thermal images  captured in different weather and 
-environmental conditions.  
-• A detailed comparative evaluation of SoA object detection 
-based on a modified  YOLO -V5 network , fine-tuned for 
-thermal images  using this newly acquired dataset .  
-• Model optimization using TensorRT inference accelerator 
-to implement a fast  inference network on SoA embedded  
-GPU boards  (Jetson, Xavier) with comparative evaluations . 
-Muhammad Ali Farooq, Peter Corcoran,  and Waseem Shariff are with the 
-National University of Ireland Galway, (NUIG), Coll ege of Science & 
-Engineering  Galway, H91TK33, Ireland (e -mail: [email protected] ,    
-[email protected] , [email protected] ).  
-Thermal Dataset Link:  https://bit.ly/3tAkJ0J        
-GitHub Link :  https://git hub.com/MAli -Farooq/Thermal -YOLO   T 2 
- 
- • A determination of realistic frame rates that can be achieved 
-for the rmal object detection on SoA embedded GPU 
-platforms .  
-II. BACK GROUND  
-ADAS  (Advanced Driver Assistance Systems) are classified 
-as AI -based intelligent systems integrated with core vehicular 
-systems to assist the driver by providing a wide range of digital 
-features for safe and reliable road journeys. Such type of system 
-is designed by employing an array of electronic sensors and 
-optical mixtures such as different types of cameras to identify 
-surrounding impediments, driver faults, and reacts 
-automatically.  
-The second part of this section will mainly summarize the 
-existing/ p ublished thermal datasets along with their respective 
-attributes. These datasets can be effectively used for training 
-and testing the machine learning algorithms for object detection 
-in thermal spectrum for ADAS. The complete dataset details are 
-provided i n Table I .  
-TABLE I  
-EXISTING THERMAL DATASETS  
-A. Related Literature  
-We can find numerous studies regarding the implementation 
-of object detection algorithms using AI based conventional 
-machine learning as well as deep learning algorithms. Such type 
-of optical imaging -based systems system can be deployed and 
-effectively used as forward sensing methods for ADAS. 
-Advanced Driver -Assistance Systems (ADAS) is an active area 
-of research that seeks to make road trips more safe and secure.  
-Real time object detection pl ays a critical role to warn the driver 
-thus allowing them to make timely decisions [ 8]. Ziyatdinov et 
-al [8] proposed an automated system to detect road signs. This method uses the GTSRB dataset [ 20] to train on conventional 
-machine learning algorithms whi ch include SVM, KNN , and 
-Decision Trees classifier. The results proved that SVM and K – 
-nearest neighbour ( k-NN) outperforms all other classifiers.  
-Autonomous cars on the road require the abilities to 
-consistently perceive and comprehend their surroundings  [9]. 
-Oliver et al [ 9] presented a procedure to use Bernoulli particle 
-filter, which is suitable for object identification because it can 
-handle a wide range of sensor measurements as well as object 
-appearance -disappearance . Gang Yan et al [ 10] proposed a 
-novel method to use HOG to extract features  and AdaBoost and 
-SVM classifiers to detect vehicles in real -time. The histogram 
-of oriented gradients (HOG) is a feature extraction technique 
-used for object detection in the domai n of computer vision  and 
-machine learning . The study concluded that the AdaBoost 
-classification technique performed slightly better than SVM 
-since it uses the ensemble method. Authors in [ 11], proposed 
-another approach to detect vehicles on road using HOG filters 
-to again extract features from the frames and then classify them 
-using support vector machines and decision tree classification 
-algorithms. Furthermore, SVM achieved 93.75% accuracy, 
-which outperformed decision tree accuracy on classifying the 
-vehicles. These are some of the conventional machine learning 
-object detection techniques used for driver assistance system till 
-date.  The main drawback of traditional machine learning 
-technique s is that  the features are extracted and predefined  prior 
-to training and tes ting of the algorithms . When dealing with 
-high-dimensional data, and with many classes conventional 
-machine learning techniques are often ineffective [ 21].  
-Deep learning approaches have emerged as more reliable and 
-effective solutions than these classic approaches. There are 
-many state -of-the-art pre -trained deep learning classifiers  and 
-object detection models which can be retrain ed and rapidly 
-deployed for  designing efficient forward sensing algorithms 
-[22]. YOLO  (you only look once) object classifier  provides 
-sufficient performance to operate at real -time speeds on 
-conventional video data without compromising the overall 
-detector precision [ 15]. Veta et al [12] presented a technique for 
-detecting objects at a distance  by employing YOLO on low -
-quality thermal images . Another research [ 13] focused on 
-pedestrian detection  in thermal images using the histogram of 
-gradient (HOG) and YOLO methods on FLIR [ 7] datas et and 
-computed performance with a 70 % accuracy on test data using 
-the intersection over union technique. Further, Rumi et al [ 14] 
-proposed a real -time human detection technique using YOLO -
-v3 on KAIST [ 5] thermal  dataset, achieving 95.5% average 
-precision on test data. Authors in [ 16] proposed a human 
-detection system using YOLO object detector. The  authors used  
-their custom dataset recorded in different weather conditions 
-using FLIR Therma -CAM P10 thermal camera.  
-Focusing  on road-side objects, authors in [ 17] used YOLO -v2 
-object detection model to enhance the recognition of tiny 
-vehicle objects by combining low -level and high -level features  
-of the image .  In [18], the authors proposed a deep learning -
-based vehicle occupancy detection  system  in a parking lot using 
-a thermal camera . In this study authors had establis hed that 
-YOLO, Yolo -Conv, GoogleNet , and ResNet18 are 
-computationally more efficient, take less processing time, and 
-are suitable for real -time object detection.  In one of the  most 
-recent studies [ 24], the efficacy of typical state -of-the-art object Datasets  Condition  
-Day Night  Annotations  Objects  Total 
-no. of 
-frames  Image 
-Resolution  
-OSU 
-Thermal 
-[2] ✓ ✓ - Person, 
-Cars, 
-Poles  284 360 X 240  
-CVC 
-[19] ✓ ✓ - Person, 
-Cars, 
-Poles, 
-Bicycle, 
-Bus, 
-Bikes  11K 640 X  480 
-LITIV 
-[3] - - - Person  6K 320 X 240  
-TIV [ 4] - - - Person, 
-Cars, 
-Bicycle, 
-Bat 63K 1024 X 
-1024  
-SCUT 
-[6] - ✓ ✓ Person  211K  384 X 288  
-FLIR 
-[7] ✓ ✓ ✓ Person, 
-Cars, 
-Poles, 
-Bicycle, 
-Bus, 
-Dog 14K 640 X 512  
-KAIST 
-[5] ✓ ✓ ✓ Person, 
-Cars, 
-Poles, 
-Bicycle, 
-Bus 95K 640 X 480  3 
- 
- 
-detectors which includes  Faster R -CNN, SSD, Cascade R -
-CNN, and YOLO -v3 was assessed by retrain ing them on a  
-thermal dataset.  The results demonstrated that Yolo -v3 
-outclassed other object SoA object detect ors.   
-B. Object Detection on Edge Devices  
-AI on edge devices benefit s us in various methods such that 
-it speeds up decision -making, makes data processing more 
-reliable, enhan ces user experience with hyper -personalization, 
-and cuts down the costs . While machine learning models ha ve 
-shown immense strength in diversified consumer electronic 
-applications , the increased prevalence of AI  on edge  has 
-contributed to the growth of spec ial-purpose embedded boards 
-for various applications.  Such type of embedded boards  can 
-achieve  AI inference at higher frames per second (fps) and low 
-power usage . Some of these board includes Nvidia Jetson Nano, 
-Nvidia Xavier, Google Coral, AWS DeepLens , and Intel AI-
-Stick . Authors in [ 26-27] proposed a r aspberry pi-based edge 
-computing system  to detect thermal objects. Sen Cao et al [ 28] 
-developed a roadside object detector using KITTI dataset [ 29] 
-by training an efficient and lightweight neural  network on 
-Nvidia Jetson TX2 embedded GPU  [28].  
-In another study [ 30] authors  proposed deep learning -based 
-smart task scheduling for self -driving vehicles. This task 
-management module was implemented  on multicore SoCs 
-(Odroid Xu4 and Nvidia Jetson).   
-The overall goal of this study is to analy se the real -time 
-performance feasibility of Thermal -YOLO object detector by 
-deploying on edge devices.  Different network variants of yolo -
-v5 framework are trained and fine -tuned on thermal image data 
-and implemented on the Nvidia Jetson Nano [ 23] and Nvidia 
-Jetson Xavier NX [ 25]. These two platforms, although from the 
-same manufacturer provide very differe nt levels of performance 
-and may be regarded as close to current SoA in terms of 
-performance for embedded neural inference algorithms.  
-III. THERMAL DATA ACQUISITION AT SCALE FOR ADAS  
-This section will mainly cover the thermal data collection 
-process using the LWIR pr ototype thermal imaging camera. 
-The overall data is consisting of more than 35K distinct thermal 
-frames acquired in different weather and environmental 
-conditions . The data collection process includes shutterless 
-camera calibration and thermal data processing [36], using the 
-Lynred Display Kit (LDK) [ 1], data collection methods , and 
-overall dataset attributes with different weather and 
-environmental conditions for comprehensive data formation.  
-A. Prototype Thermal Camera  
-For the proposed research  work we have utilized micro -
-bolometer technology based uncooled thermal imaging camera 
-developed under the HELIAUS project [ 32]. The main 
-characteristic of this camera includes low -cost, lightweight and 
-its sleek compact design thus allowing to easily integrate it with 
-artificially intelligent imaging pipelines for building effective 
-in-cabin driver -passenger monitoring and road mon itoring 
-systems for ADAS. It enables us to capture high -quality thermal 
-frames with low -power consumption thus proving the agility of 
-configurations and data processing algorithms in real -time.   
-Fig. 1 shows the prototype thermal camera. The technical 
-specifications of the camera are as follows, the camera type is a QVGA long-wave infrared (LWIR) with a spectral range from 
-8-14 µm and a camera resolution of 640 X 480 pixels. The focal 
-length (f) of the camera is 7.5 mm, F -number is 1.2, the pixel 
-pitch is 17 µm, and the power consumption is less than 950mW. 
-The camera relates to a high-speed USB 3.0 (micro -USB)  port 
-for the interface.  
- 
- 
- 
- 
- 
- 
-Fig. 1 . LWIR thermal imaging module images from different 
-view angles.  
- 
-The data is recorded using a specifically designed toolbox. The 
-complete camera calibration process along with the data 
-processing pipeline is explained in the next section.  
-B. Shutterless Calibration and Real -time Data Processing  
-This section will highlight the thermal camera calibration 
-process for shutterless camera configuration along with real -
-time data processing methods for converting the raw thermal 
-data to refined output s. Shutterless technology allows uncooled 
-IR engines and thermal imaging sensors to continuously operate 
-without the need for a mechanical shutter for Non -Uniformity 
-Correction (NUC) operations. Such type of technology 
-provides proven and effective results  in poor visibility 
-conditions ensuring good quality thermal frames in real -time 
-testing situations. For this, we have used a low -cost blackbody 
-source to provide three different constant reference temperature 
-values referred to as T -ambient1 -BB1 (hot unif orm scene with 
-temperature value of 40 degree centigrade), T -ambient1 -BB2 
-(cold uniform scene with the temperature value of 20 degree 
-centigrade), and T -ambient2 -BB1 (either hot or cold uniform 
-scene but with different temperature value). The  imager  can 
-store up to 50 snapshots and select the best uniform temperature 
-scenes for calibration purposes. Fig. 2  shows the blackbody 
-used for the thermal camera calibration .  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 2.  Thermal camera calibration a) blackbody source used 
-for LWIR thermal camera calibration, b) uniform scene: 
-temperature set to 40.01 degree centigrade.  
- 
-Once the uniform temperature images are recorded the images 
-are loaded in camera SDK as shown in Fig. 3 to finally calibrate 
-the shutterless camera stream. Fig. 4 shows the results before 
-applying shutterless calibration and processed results using 
-shutterless algorithms on thermal frame capture through the 
-prototype thermal IR camera . (a) (b) 4 
- 
-   
-Fig. 3. Prototype thermal camera SDK for loading constant 
-reference temperatures values for shutterless camera 
-calibration.  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 4.  Shutterless algorithm results on sample thermal frame 
-captured from 640x480 LWIR thermal camera designed by 
-Lynred France [ 1]. 
- 
-In the next phase, various real -time image processing -based 
-correction methods are applied to convert the original thermal 
-data to produce good -quality thermal frames. Fig. 5 shows the 
-complete image processing pipeline.  
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 5.  Thermal image correction pipeline  
- 
-As shown in Fig. 5 image processing pipeline consist of three 
-different image correction methods which include gain 
-correction, bad -pixel replacement, and temporal denoising. The 
-further details of these methods are provided as follows.  
- 
-1) Gain Correction Automatic Gain Control (AGC)  
-Thermal image detectors, based on flat panels, suff er 
-from irregular gains due to the non -uniform amplifiers. 
-To correct the irregular gains, a common yet effective 
-technique referred to as automatic gain control is 
-applied. It is usually based on the gain map. By 
-averaging uniformly illuminated images wit hout any 
-objects, the gain map is designed. By increasing the 
-number of images for averaging provides a good gain -correction performance since the remained quantum 
-noise in the gain map is reduced [ 1]. 
- 
-2) Bad Pixel Replacement (BPR)  
-This is used to list bad pixels estimated at the calibration 
-stage. It works by tracking potential new bad pixels by 
-looking at pixel neighbourhood also known as the 
-nearest neighbour method. Once it traces the bad pixels 
-in the nearest neighbour it replaces them with good 
-pixels. Fig. 6 demonstrates one  such example.   
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 6.  Bad pixel replacement algorithm output on 
-sample thermal frame, left side frame with some bad 
-pixels and the right side is processed frame.  
- 
-3) Temporal Denoising (TD)  
-The consistent reduction of image noise poses a 
-frequently recurring problem in digitized thermal 
-imaging systems and especially when it comes to un -
-cooled thermal imagers [ 34]. To mitigate these 
-limitations for better outputs different methods are used 
-which include hardware as well  software -based image 
-processing methods such as temporal and spatial 
-denoising algorithms. The temporal denoising method is 
-used to decrease the temporal noise between different 
-frames of the video. In commercial solutions, it usually 
-works by gathering m ultiple frames and averaging those 
-frames to cancel out the random noise among the frames. 
-In our data acquisition process, this method is used after 
-applying the shutterless algorithm. Fig. 7  shows the 
-sample thermal images in the form of outcomes after 
-applying shutterless algorithms and all the image 
-processing -based corrections methods as shown in Fig. 
-5. 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 7. High -quality thermal frames after applying the 
-shutterless calibration algorithm and image correction 
-methods.  
-Before  After   
-Shutterl
-ess 
-thermal 
-data 
-Gain 
-correction  
-Bad-pixel 
-replacement  
-Temporal 
-denoising  
-Image processing 
-pipeline  
-Output  
-Input  Output   
-5 
- 
- C. Data Collection Methods and Overall Dataset Attributes  
-This section will highlight different data collection 
-approaches adopted in this research work. The data is collected 
-in two different approaches. In, the first approach (M -1) the data 
-is gathered in an imm obile method by placing the camera at a 
-fixed place. The camera is mounted on the tripod stand at a 
-fixed height of nearly 30 inches such that the roadsides objects 
-are covered in the video stream. The thermal video stream is 
-recorded at 30 frames per seco nd (FPS). The data is recorded in 
-different weather and environmental conditions. Fig. 8 shows 
-the M -1 data acquisition setup. In the second method (M -2) the 
-thermal imaging system is mounted over the car and data is 
-acquired in the mobile method. The prime reason for collecting 
-the data in two different methods is to bring variations and 
-collect distinctive local data in different environmental and 
-weather conditions. For this, a specialized waterproof camera 
-housing case was designe d to hold the thermal camera in the 
-correct position and angle to cover the entire roadside scene. 
-The housing case is fixed on a suction -based tripod stand thus 
-allowing us to easily fix and remove the complete structure 
-from the car bonnet. The housing c ase also contains a visible 
-camera to get initial visible images as reference data thus 
-allowing us to adjust both the camera positions in proper  angle 
-and field of view .  
- 
- 
-Fig. 8.   Data Acquisition setup by placing the camera at a fixed 
-place a) camera mounted on a tripod stand, b) complete daytime 
-roadside view, c) video recording setup at 30fps, d) evening 
-time alleyway view.  
- 
-Fig. 9 shows the camera housing case along with the initial 
-data acquisition setup whereas  
- 
-                                
-Fig. 9.  Data acquisition setup through car a) camera housing 
-case holding thermal and visible camera, b) initial data 
-acquisition testing phase.  
- 
-Fig. 10 shows the housing case fixed on tripod structure and 
-complete M -2 acquisition setup mounted on the car. The overall 
-dataset is acquired from Galway County Ireland. The data is 
-collected in form of short video clips and more th an> 35,000 
-unique thermal frames have been extracted from the recorde d 
-video clips. The data is recorded in the daytime, evening time , 
-and night -time which is distributed in the ratio of 44. 61%, 31.78%, and 23. 61% respectively  of overall data. The complete 
-dataset attributes are summarized in Table II. The acquired data 
-comprises  distinct stationary  classes , such as road signs and 
-poles,  as well as moving object  classes  such as  pedestrians, cars, 
-buses, bikes, and bicycles.  
- 
- 
-Fig. 10.   Complete data acquisition setup mounted on the car a) 
-camera housing fixed on a suction tripod stand, b) data 
-acquisition kit from the front view, c) data acquisition kit from 
-the side view.  
-TABLE II  
-NEW THERMAL DATASET  ATTRIBUTES  
-Locally acquired dataset attributes   
-Data 
-collection 
-method with 
-frame 
-properties  Total 
-number 
-of 
-extracted 
-frames  Processing 
-Method  Environment  Time and 
-weather 
-conditions  
-M-1 
-Camera 
-mounted at a 
-fixed place  
- 
-96 dpi 
-(horizontal 
-and vertical 
-resolution) 
-with 
-640x480 
-image 
-dimension  8,140  
- Shutterless, 
-AGC, BPR, 
-TD 
- Roadside  Daytime 
-with cloudy 
-weather  
-680 Alleyway  Evening 
-time cloudy 
-weather  
-4,790  Roadside  Night -time 
-with light 
-cloudy and 
-windy 
-weather  
-M-2 
-Camera 
-mounted on 
-the car  
-(Driving 
-condition)  
- 
-96 dpi 
-(horizontal 
-and vertical 
-resolution) 
-with 
-640x480 
-image 
-dimension  
- 9,600 Shutterless, 
-AGC, BPR, 
-TD 
- Industrial 
-Park Daytime 
-with clear 
-weather 
-and light 
-foggy 
-weather  
-11,960  Downtown  Evening 
-time with 
-partially 
-cloudy and 
-windy 
-weather  
-4,600  Shutterless, 
-AGC, BPR,  
-& TD  Downtown  Night -time 
-with clear 
-weather 
-conditions  
-frames  Daytime: 
-17,740 
-(44.61%) Evening 
-time: 
-12,640 
-(31.78%) Night -time: 
-9,390 
-(23.61%) Total: 
-39,770   
-Fig. 11 shows the six distinct sample of thermal frames captured 
-in different environmental and weather conditions using M1 
-and M2 methods.  These samples show different class object s 
-such as buses, bicycles , poles, person,  and cars.  Most of these 
-objects are found commonly on the roadside  thus providing the 
-driver a comprehensive video analysis of car surroundings . 
-(a) 
- (b) (c) (d) 
-(a) (b) (a) (b) (c) Suction Tripod 
-structure  
-640x480 LWIR 
-thermal camera  6 
- 
-  
- 
- 
-Fig. 11.  Six different thermal samples acquired using LWIR 
-640x480 prototype thermal camera showing various class 
-objects.   
-IV. PROPOSED METHODOLOGY  
-This section will detail the proposed methodology and 
-training outcomes from the various network variants tested in 
-this study.  
-A. Network Training and Learning Perspectives  
-The overall training data comprises both locally and publicly 
-available datasets. The complete training data is divided in the 
-ratio of 50% - 50% where 50% of data is selected from locally 
-acquired t hermal frames whereas the rest 50% of the training 
-data leverages  from public datasets. Six distinct types of 
-roadside objects for driving assistance are included in training 
-and validations sets . These include b icycles, motor cycles , 
-buses, cars, pedestrians or people, and static roadside  objects 
-such as  poles  or road signs , as shown  in Fig 1 2. 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 12.  Block diagram depicts the steps taken to evaluate the 
-performance of Yolo v5 on local and public datasets.  
- Fig. 13 shows the class -wise data distribution. In the training 
-phase of the YOLO -V5 framework, a total of 59,150 class -wise 
-data samples wer e utilized, along with their corresponding class 
-labels  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 13.  Depicts the respective class -wise training samples 
-distributions . 
-B. Data Annotation and Augmentation  
-The overall data annotations were performed manually using 
-an open -source bounding box -based annotations tool LabelImg 
-[31] for all the thermal classes in our study. Annotations are 
-stored  in YOLO format as text files. During the training phase 
-all the YoloV5 network variations which include small, 
-medium, large, and x -large networks have been trained to detect 
-and classify six different classes in different environmental 
-conditions.  
-Large -scale datasets are considered a vital requirement for 
-achieving optimal training results using deep learning 
-architectures. Without the need of gathering new data, data 
-augmentation allows us to significantly improve the diversity 
-of data available that c an be effectively used for training the 
-DNN models. In the proposed study we have incorporated a 
-variety of data augmentation techniques which involve 
-cropping, flipping, rotation, shearing, translation, mosaic 
-transformation for an optimum training of all  the network 
-variants of the YOLO -V5 framework . 
-A. C. Training Results  
-As discussed in subsection A of section IV all the networks 
-are trained using the combination of public as well as the locally 
-gathered dataset. Training data from public datasets are 
-included from four different datasets which include FLIR [ 7], 
-OST [ 2], CVC [ 19], and KAIST [ 5] datasets. Secondly, we have 
-used thermal frames acquired from the locally gathered video 
-sets using both M1 and M2 methods. The training process is 
-performed on a server -grade machine with XEON E5 -1650 v4 
-3.60 GHz processor, 64 GB of ram, and  equipped with 
-GEFORCE RTX 2080  Ti graphical processing unit. It comes 
-with 12 GB of dedicated graphical memory, memory bandwidth 
-of 616 GB/second, and 4352 cuda cores . During  the training 
-phase the batch size is fixed to 32 and as an optimizer, both 
-stochastic gradient descent  (SGD) and ADAM optimizer were 
-used. However, we were unable to achieve satisfactory training 
-results using ADAM optimizer as compared to SGD thus 
-select ed SGD optimizer for training purposes. Table  III shows 
-the performance evaluation of all the trained models in the form 
-Training Data                           
-Locally Acquired + Public 
-Datasets  
-Classes  
- 
-Car, Pole, Bike, Bicycle, Person, Bus
-  
-Data Annotation  
-Data Augmentation Techniques  
- 
-Flipping, Cropping, Shearing, Rotation, Translation, Mosaic  
-YOLO v5 Network Variants  
- 
-Small  
-(7.3 million 
-parameters ) 
-Medium  
-(21 million 
-parameters ) 
-Large  
-(47 million 
-parameters ) 
-X-Large 
-(87.7m illio
-n 
-parameters ) 
-Inference Testing  (On both public and local dataset)  
- 
-GPU, Nvidia -Jetson, Nvidia -Xavier  
-7 
- 
- of mean average precision (mAP), recall rate, precision, and 
-losses.  
-TABLE III 
-TRAINING RESULTS  
-Optimizer: SGD  (best model  *)  
-Network  P % R% mAP
-% Box 
-Loss  Object 
-Loss  Classific
-ation 
-Loss  
-Small  75.5
-8 65.75  70.71 0.03
-2 0.034  0.0017  
-Medium  71.0
-6 64.74  65.34 0.02
-7   0.030  0.0013  
-Large  * 82.2
-9 68.67  71.8 0.02
-5   0.0287  0.0011  
-X-Large  74.2
-3 65.03  64.94  0.02
-5  0.0270  0.0010  
- 
-By analy sing Table III, it can be observed that the large model 
-performed significantly better when compared to other models 
-with an overall precision of 82.29%, recall rate of 68.67%, and 
-mean average precision of 71.8% mAP . Fig.  14 shows the 
-graph result s of yolo-v5 large model.  The figure visualizes 
-obtained PR -curve, box loss, object loss, and classification loss. 
-During the tra ining process, the X -large model consumes the 
-maximum amount of hardware resources with the largest 
-training time as compared to other network variants with overall 
-GPU usage of 9.78 GB and a total training time of 14 hours . 
-Fig. 15 shows the overall GPU m emory usage, GPU power 
-required in percentages, and GPU temperature in centigrade 
-scale while training the largest x -large network variant of yolo -
-v5 model.  
- 
- 
- 
- 
- 
- 
-Fig. 14.  Training results of YOLO -v5 large model  using SGD  
-optimizer . 
-V. VALIDATION RESULTS ON GPU  AND EDGE DEVICES  
-This section will demonstrate the object detection validation 
-results on GPU as well as on two different embedded boards.  
-A. Testing Methodology and Overall Test Data  
-    In this research study , we have used three different testing 
-approaches which include the conventional test -time method 
-with no augmentation (NA), test -time augmentation (TTA), and 
-test-time with model ensembling (ME). TTA is an extensive 
-application of data augmentation applied  to the test dataset. It 
-performs by creating multiple augmented copies of each image 
-in the test set, having the model make a prediction for each, then 
-returning an ensemble of those predictions.  However, since the 
-test dataset is enlarged  with a new set of augmented images the   
- 
- 
- 
-Fig. 15. GPU resource utilization during the training process of 
-x-large network, a) 85% (9.78 GB) of GPU memory utilized, 
-(b) 90% (585 watts) of GPU power required and, (c) 68 C of 
-GPU temperature with the maximum rating of 89 C.   
- 
-overall inference time also increases as compared to NA which 
-is one of the downsides of this approach . TTME or ensemble 
-learning refers to as using multiple trained networks at the same 
-time in a parallel  manner to produce one optimal predictive  
-inference model  [35]. In this  study, we have tested the 
-performance of individually trained variants of the Yolo -V5 
-framework and selected the best combination of models which 
-in turn helps in achieving better  validation results.  
-After training all the networks variants of yolo -v5, the 
-performance of each model is cross -validated on a 
-comprehensive set of test data  selected from the public as well 
-as locally gathered thermal data. Table IV provides the numeric 
-data distribution of the overall validation set.  
-TABLE IV 
-TEST DATASET  
-Test Dataset Attributes  
-Frames Used  
-Public 
-dataset  OST  CVC -09 KAIS
-T FLI
-R Total No 
-frames  
- 
- 
-50 5360 
-(day +  
-night -
-time)  149 130 5,689  
-Local 
-dataset  Method (M1)  Method (M2)  Total No 
-frames  
-a 
-b 
-c 100 
- 80 
- 60 
- 40 
- 20 
- 60  120  20 minutes  GPU Memory Usage  
-GPU Power Usage  
-GPU Temperature   20  60  120 minutes   20  40  60  80  100 
-minutes   20  60  120  10  20  30  40  50  60 8 
- 
- 
- 
-8,820 16,560  25,380  
- Total: 31,069  
-B. Inference Results Using YOLO Network Variants  
-In the first phase, we have run the  rigorous inference test on 
-GPU as well as Edge -GPU platforms on our test data using the 
-newly trained networks variants of yolo framework. The overall 
-test data is consisting  of nearly ≈ 31,000 thermal frames. Fig. 1 6 
-shows the inference results on 9 diffe rent thermal frames 
-selected from both public as well as locally acquired data. These 
-frames have data complications such as multiple class objects, 
-occlusion, overlapping classes, scale variation, and varying 
-environmental conditions . The complete inference results are 
-available on our local repos itory ( https://bit.ly/3lfvxhd ).  
-In the second phase , we have run t he combination of different 
-models in a parallel manner using the model ensembling 
-approach to output one optimal predictive engine which can be 
-further used to run the inference test on the validation set. The 
-different combination of these models is show n in Table V  
-respectively where 1 indicates that model is in active state and 
-0 means model is in a non -active state.  
-TABLE V 
-MODEL ENSEMBLING  
-Model Combinations  
-N
-o  Small   Medium  Large  X-Large  Combination  
-State 1 (active) or 0 (not active)  
-1 1 1 0 0 A0 
-2 1 0 1 0 A1 
-3 1 0 0 1 A2 
-4 0 1 1 0 A3 
-5 0 0 1 1 A4 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 16 . Inference results on nine different frames selected 
-from test data.  
- 
-With the model ensembling method small and large models 
-(A1) turn out to best model combination in terms of achieving 
-the best mAP, recall, and relatively less amount of inference 
-time per frame thus producing optimal validation results. These 
-results are examined in further parts of this section.  Fig. 1 7 
-shows the inference results using A1 model ensembling engine 
-on three  different thermal frames s elected  from the test data.   
- 
- 
- 
- 
- 
- 
-Fig. 17.  Inference results on three different frames using 
-model ensembling.  
-C. Quantitative Validation Results on GPU  
-The third part of the testing phase shows the quantitative 
-numerical results of all the trained models on GPU. To better 
-analy se and validate the overall performance  for all the trained 
-models  on test data, relatively a smaller set of test images has 
-been selected from the overall test  set. For this purpose, a subset  
-of 402 thermal frames  is selected to compute all the evaluation 
-metrics. The selected images consist of different roadside 
-objects such as pedestrians, cars and buses under different 
-illum ination and environmental conditions, time of day, and 
-distance from the camera. The objects are either far -field 
-(between 11 -18 meters) , mid -field (between 7 -10 meters) or 
-near-field (between 3 -6 meters) from the camera. Fig. 18 shows 
-selected views from the test data for quick reference of the 
-reader.  
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 18.  Test data samples with the object at varying distances 
-from the camera, (a) near -field distance, (b) mid -field distance, 
-(c) far -field distance.  
- 
-The performance evaluation of each model is computed 
-using four different metrics which include recall, preci sion, 
-mean average precision (mAP), and frames per second rate 
-(FPS). Table VI shows all the quantitative validation results on 
-GPU. During the testing phase batch size is fixed to 8. Also, 
-three different testing configuration is selected thus having 
-separate confidence threshold values and the intersection of 
-union values at each validation phase. Confidence threshold 
-defines the minimum threshold value, or in other words, it is the 
-minimum confidence score above which we consider a 
-prediction as true. If  it’s below the threshold value, we consider 
-the prediction as “no”. The last row of Table VI  shows the best 
-ME results using A1 configuration from Table V  with a selected 
-confidence threshold of 0.2 and IoU threshold of 0.4.  
-TABLE VI 
-QUANTITATIVE RESULTS ON GPU  
-Platform: GPU  
-Inference image size: 800 x 800  
-Confidence Threshold: 0.4, IoU Threshold: 0.6  
-No Augmentation (NA)  Test-time Augmentation 
-(TTA)  
-Network  P 
-% R
-% mA
-P% FPS P 
-% R
-% mA
-P% FPS 
-a b c camera  camera  camera  person  person  car 9 
- 
- Small  72 46 43 79 76 48 50 45 
-Medium  73 54 49 53 76 58 57 26 
-Large   75 56 52 34 77 63 60 16 
-X-Large  74 53 49 20 71 59 55 10 
-Confidence Threshold: 0.2, IoU Threshold: 0.4  
-NA TTA 
-Small  66 50 47 82 64 55 52 45 
-Medium  66 57 51 53 77 58 59 27 
-Large   71 61 56 35 78 63 63 16 
-X-Large  70 54 50 21 68 62 56 10 
-Confidence Threshold: 0.1, IoU Threshold: 0.2  
-NA TTA 
-Small  65 52 48 81 65 53 53 45 
-Medium  69 54 51 53 77 58 59 26 
-Large   73 61 57 34 79 63 63 16 
-X-Large  71 54 52 21 69 62 57 10 
-Confidence Threshold: 0.2, IoU Threshold: 0.4  
-Model Ensembling (ME)  
-A = Small  
-B = Large  
-Comb: A1  
- --- --- --- --- 77 66 65 25 
- 
-D. Quantitative Validation Results on Edge -GPU Devices  
-This section will review the quantitative validation results on 
-two different Edge -GPU platforms (Jetson Nano & Jetson 
-Xavier NX).  It is pertinent to mention that Jetson Xavier NX 
-development kit embeds more computational power in terms of 
-GPU, CPU, and memory as compared to Nvidia Jetson Nano. 
-Table  VII shows the specification comparison of both boards.  
-TABLE VII 
-HARDWARE SPECIFICAT ION COMPARISON  
-Hardware specification comparison o f Nvidia Jetson Nano and 
-Nvidia Jetson Xavier NX 
-Board Jetson Nano [23] Jetson Xavier N X [25] 
-CPU  Quad -Core  ARM® 
-Cortex® -A57 MPCore, 
-2 MB L2,  Maximum 
-Operating 
-Frequency:  1.43 GHz  6-core NVIDIA Carmel 
-ARM®v8.2 64 -bit 
-CPU, 6 MB L2 + 4 MB 
-L3, Maximum 
-Operating 
-Frequency:  1.9 GHz  
-GPU  128-
-core Maxwell  GPU, 
-512 GFLOPS (FP16), 
-Maximum Operating 
-Frequency: 921 MHz  384 CUDA® cores + 
-48 Tensor 
-cores  Volta  GPU, 21 
-TOPS, Maximum 
-Operating Frequency: 
-1100 MHz  
-RAM  4 GB  64-bit LPDDR4 
-@ 1600MHz | 25.6 
-GB/s  8 GB  128-bit 
-LPDDR4x @ 
-1600MHz | 51.2GB/s  
-On module 
-Storage  16 GB  eMMC 5.1 Flash Storage, Bus Width: 8 -bit, 
-Maximum Bus Frequency: 200 MHz (HS400)  
-Thermal 
-Design 
-Power  5W – 10W  10W – 15W  
-AI 
-Performance  0.5 TFLOPS (FP16)  6 TFLOPS (FP16)  
-21 TOPS (INT8)  
- 
-On Jetson Nano we have validated the performance of the 
-small version only whereas on Jetson Xavier NX we have 
-evaluated the performance of smaller and medium versions of 
-models due to the memory limitations and constrained 
-hardware resources on these boar ds. During the testing phase, 
-we have selected the highest power modes on both boards to 
-provide the utmost efficiency thus utilizing maximum hardware 
-resources. For instance, on Nvidia Xavier board NX we have selected ‘Mode Id: 2’ which means the board is  operating in 15 -
-watt power mode with all the six cores active with a maximal 
-CPU frequency of 1.4 gigahertz and GPU frequency of 1.1  
-gigahertz. Similarly, on Nvidia Jetson Nano all the four CPU 
-cores were utilized with overall power utilization of 5 watts . 
-Table  VIII shows the quantitative validation results  on ARM 
-processor based embedded boards  
-TABLE VIII 
-QUANTITATIVE RESULTS ON EDGE PLATFORMS  
-Platform: N vidia Jetson Nano  
-Inference image size: 128 x 128  
-Confidence Threshold: 0.4, IoU Threshold: 0.6  
-NA TTA  
- P 
-% R
-% mA
-P% FPS P 
-% R
-% mA
-P% FPS 
- 
-Small  75 44 45 3 77 47 49 1 
-Confidence Threshold: 0.2, IoU Threshold: 0.4  
-NA TTA  
-Small  75 44 47 3 71 51 51 1 
-Confidence Threshold: 0.1, IoU Threshold: 0.2  
-NA TTA  
-Small 66 47 48 2 73 50 52 1 
-Platform: N vidia Jetson  Xavier NX  
-Inference image size: 128 x 128  
-Confidence Threshold: 0.4, IoU Threshold: 0.6  
-NA TTA  
-Small  75 44 45 18 77 47 49 10 
-Med 76 53 50 12 79 50 52 6 
-Confidence Threshold: 0.2, IoU Threshold: 0.4  
-NA TTA 
-Small  75 44 47 19 71 51 51 10 
-Med 76 52 53 12 73 54 53 6 
-Confidence Threshold: 0.1, IoU Threshold: 0.2  
-NA TTA 
-Small  66 47 48 18 73 50 52 10 
-Med 76 51 52 12 81 49 53 6 
-E. Real-time Hardware Feasibility Testing  
-While running these tests we closely monitor the temperature 
-ratings of different hardware peripherals on both Edge -GPU 
-platforms. It is done to prevent the overheating effect which can 
-damage the onboard processor or effect the overall operational 
-capabil ity of the system. In the case of Nvidia Jetson Nano, a 
-cooling fan was mounted on top of the processor heatsink to 
-reduce the overheating effect as shown in Fig. 1 9. 
- 
- 
-Fig. 19.  External 5 -volt fan unit mounted on Nvidia Jetson 
-Nano processor heatsink to avoid onboard overheating effect 
-while running the inference testing.  
-The temperature ratings of various hardware peripherals are 
-monitored using eight different on -die thermal s ensors and one 
-on-die thermal diode. These temperature monitors are referred 
-to as CPU -Thermal, GPU -Thermal, Memory -Thermal, and 
-PLL-Thermal (part thermal zone). External fans help us in 
-External fan  10 
- 
- 
-reducing the temperature rating of various hardware peripherals 
-drast ically as compared to without mounting the fan. Fig. 20 
-shows the temperature rating difference of onboard thermal 
-sensors while running the smaller version of the model on 
-Nvidia Jetson Nano without and with mounting the external 
-cooling fan.  
- 
- 
- 
- 
- 
-Fig. 20.  Temperature rating difference of different onboard 
-hardware peripherals on Jetson Nano (a) without fan: A0 
-thermal zone = 65.50 C, CPU = 55 C, GPU = 52 C, PLL: 53.50, 
-overall thermal temperature = 53.50 C, (b) with external fan: 
-A0 therm al zone = 45.50 C, CPU = 33 C, GPU = 33 C, PLL: 
-33, overall thermal temperature = 32.75 C.  
- 
-It can be examined from Fig. 20 that by mounting an external 
-cooling fan the temperature rating of  various onboard 
-peripheral on Jetson Nano was reduced by nearly 30% thus 
-allowing us to operate the board at its maximum capacity for 
-rigorous model testing. Fig. 21 shows the Nvidia Jetson running 
-at its full pace  (with an external fan)  such that all the four cores 
-running at their maximum limit (100% capacity) while ru nning 
-the quantitative  and inference test by deploying the smaller 
-network variant of the yolo -v5 framework.   
- 
-Fig. 21.  Nvidia Jetson Nano running at MAXN power mode 
-with all the cores running at their maximum capacity while 
-running t he inference test and quantitative validation test.  
-Fig. 22 shows the temperature rating difference of onboard 
-thermal sensors while running the smaller version of the model 
-on Nvidia Jetson Xavier NX board. Whereas Fig. 23 shows the 
-CPU and GPU usage while running the smaller variant of YOLO -V5 framework for quantitative validation and inference 
-test on Nvidia Xavier NX development kit.  
- 
-Fig. 22.  Temperature rating of different onboard hardware 
-peripherals on Jetson Xavier NX (a) A0 thermal zone = 41.50 
-C, AUX: 42.5 C, CPU = 44 C, GPU = 42 C, overall thermal 
-temperature = 42.80 C,  
- 
- 
- 
- 
- 
-Fig. 23.  Nvidia Jetson Xavier running at 15 -watt 6 core power 
-mode, (a) all the CPU cores running at its maximum capacity 
-while running the quantitative validation test, (b) 69% GPU 
-utilization while running the inference test with an image size 
-of 128 x 128.  
-VI. MODEL PERFORMANCE OPTIMIZATION (S)  
-This section will mainly aim at further model optimization 
-using TensorRT [ 33] inference accelerator tool. The prime 
-reason for this is to further increase the FPS rate  for real -time 
-evaluation and on -board feasibility te sting on edge devices.  
-Secondly, it helps in saving onboard memory footprints on the 
-target device by performing various optimization methods.  
-TensorRT  [33] works by performing five modes of 
-optimization  methods  for increasing the throughput of deep  
-neural networks . In the first step, it maximizes throughput by 
-quantizing models to 8 -bit integer data type or FP16 precision 
-while preserving the model accuracy. This method significantly 
-(a) 
-(b) 
-(a) 
-(b) 11 
- 
- 
-reduces the model size since it is transformed from originally 
-FP32 to FP16 version. In the next step, it uses layer and tensor 
-fusion  techniques to further optimize the usage of onboard GPU 
-memory. The third step includes perform ing kernel auto -tuning.  
-It is the most important step where the TensorRT engine 
-shortlists  the best network layers,  and optimal batch size based 
-on the target GPU  hardware. In the second last step, it 
-minimizes memory footprint s and re -uses memory by 
-distributing memory to tensor only for the period of its usage.  
-In the last steps, it processes m ultiple input streams in parallel 
-and finally optimizes neural networks periodically with 
-dynamically generated kernels [ 33]. 
-In the proposed research work we have deployed a smaller 
-variant of yolo -v5 using TensorRT inference accelerator on 
-both edge plat forms Nvidia Jetson Nano and Nvidia Jetson 
-Xavier NX development boards to further excel the 
-performance of the trained model.  It produces faster inference 
-time thus increasing the FPS on thermal data which in turn helps 
-us in building an effective real -time forward sensing system for 
-ADAS embedded  applications.  Fig. 24 depicts the block 
-diagram representation of deployment phase TensorRT 
-inference accelerator on embedded platforms.  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Fig. 24. Overall block diagram representation of deployment 
-and running TensorRT inference accelerator on two different 
-embedded platforms.  
- 
-Table  IX shows the overall inference time along with FPS 
-rate on thermal test data using TensorRT  run-time engine.  By 
-analyzing the results from Table. IX we can deduce that 
-TensorRT API supports in boosting the overall FPS rate on 
-ARM -based embedded platforms by nearly 3.5 times as 
-compared to the FPS rate achieved by running the non -
-optimized smaller variant on Nvidia Jetson Nano and Nvidia 
-Jetson Xavier boards. The same is demonstrated via graphical 
-chart results in Fig. 2 5. 
-TABLE IX 
-TENSOR RT INFERENCE ACCELERATOR RESULTS  
-FPS on Nvidia Jetson Nano and Nvidia Jetson Xavier NX 
-Board Nvidia Jetson Nano  Nvidia Jetson Xavier 
-NX Test Data  402 images with the resolution of 128x128  
-Overall 
-inference time  35,090 milliseconds 
-≈ 35.1 seconds  6,675 milliseconds ≈ 
- 6.7 seconds  
-PS 35.1 sec / 402 frame s 
-= 0.087 sec/frame  
- 
-FPS: 1 sec / 0.087 = 
-11.49 ≈ 11 fps  6.7 sec / 402 frames = 
-0.0166 sec/frame  
- 
-FPS:  1 sec / 0.0166 = 
-60.24 ≈ 60 fps  
- 
- 
- 
-Fig. 25.  FPS increment rate of nearly 3.5 times on Jetson Nano 
-and Jetson Xavier NX embedded boards using the TensorRT 
-built optimized inference engine.  
- 
-Fig. 2 6 shows the  thermal object detection inference results on 
-six different thermal frames from the public as well as locally 
-acquired test data produced  through the neural accelerator.  
-   
- 
- 
-Fig. 2 6. Inference results using TensorRT neural accelerator, 
-(a) Object detection results on public data, (b) Object Detection 
-results on locally acquired thermal frames.  
-DISCUSSION / ANALYSIS  
-This section  will review the training and testing performance 
-of all YO LO-V5 framework model variants.  
-• During the training phase, the large YOLO v5 network 
-outperforms other network variants scoring the highest 
-precision of 82.29 % and a mean average precision (mAP) 
-score of 71.8 %.  
-• Although  the large network variant performed  significantly 
-better  during the training phase , the small network variant 020406080
-N V ID I A  J E T S O N  N A N O N V ID I A  J E T S O N  
-X A V IE R  N XF P S  C O M P A R A S IO N  U S IN G  O P T IM IZ E D  
-A N D  N O N -O P T IM I Z E D  V E R S IO N  O F  
-S M A LL N E T W O R K  V A R IA N T
-Non-optimized version TensorRT optimized version
-Smal ler variant 
-training on 
-thermal data 
-GPU  
-Trained 
-weights  
-TensorRT Inference Engine  
-1. Nvidia Jetson Nano  
- 
-2. Nvidia Jetson Xavier NX  
- 
-Jetson optimized  
-runtime engine  
-Xavier optimized  
-runtime engine  
-Test data  
-(a) 
-b 12 
- 
- also performed well with an overall precision of 75.58 % and 
-mAP of 70.71%.  Also, it gains a higher FPS rate on GPU 
-during the testing phase as compared to the large model.  
-Fig. 2 7 summarizes the quantitative performance 
-comparison of small and large network variants of yolo 
-framework.  
- 
-Fig. 2 7. Quantitative metrics comparison of small and large 
-network variants  
-• Due to the smaller number of model parameters as 
-compared to larger network variant ( 7.3M Vs 47M model 
-parameters)  and faster FPS rate on GPU during the testing 
-phase  as shown in  Fig. 26  this model is shortlisted for 
-validation and deployment purposes on both the edge 
-embedded platforms Nvidia Jetson Nano and Nvidia  Jetson 
-Xavier NX kits.  
-• During the testing phase, it was  noticed that by reducing the 
-confidence threshold from 0.4 to 0.1 and the IoU threshold 
-from 0.6 to 0.2 in three  stepwise intervals,  the model's mAP 
-and recall rates increased significantly, but the precision 
-level decreas es. However, the FPS rate remains effectively 
-constant in most of the trained model cases.  
-• TTA methods achieved improved testing results when 
-compared to the NA method however the main drawback of 
-this method is that the FPS rate dro ps substantially which is 
-not suitable for real -time deployments. To overcome this 
-problem a model ensembling (ME) based inference engine 
-is proposed. Table IV shows the ME results by running 
-large -small model in parallel configuration with a 
-confidence threshold  of 0.2, and an IoU Threshold  of 0.4. 
-The ensembling engine attains an overall mAP of 66% with 
-25 frames per second.  
-• When comparing the individual hardware resources of both 
-the edge platforms (NVidia Jetson Nano and Jetson Xavier), 
-Xavier is computationally more powerful than the Jetson 
-Nano. Note that d ue to memory limitations and the lower 
-computational power of the Jetson  only the small network 
-variant was evaluated on  the Jetson Nano, whereas both the 
-smaller and medium  network variants we re evaluated on  the 
-Jetson Xavier NX.  
-• It was observed that t hroughout the testing  phase , it was 
-important to keep a close eye on the operational temperature 
-ratings of different onboard thermal sensors  to avoid 
-overheating, which might damage the onboard components or affect the system's typical operational performance.  
-Active cooling fans were used on both boards during testing, 
-and both ran at close to their rated temperature limits.   
-• This study also included model optimization using 
-TensorRT [33] inference accelerator tool. It was  determine d 
-that TensorRT leads  to an approximate increase of FPS rate 
-by a factor of 3.5 when compared to the non-optimized 
-smaller variant of yolo -v5 on Nvi dia Jetson Nano and 
-Nvidia Jetson Xavier devices.  
-• After performing model optimization, the Nvidia Jetson 
-produced 11 FPS and Nvidia Jetson Xavier achieved 60 FPS  
-on test data . 
-CONCLUSION  
-Thermal imaging provides superior and effective results in 
-challeng ing environments such that in low lighting scenarios 
-and has aggregate immunity to visual limitations  thus making it 
-an optimal solution for intelligent and safer vehicular systems . 
-In this study, we presented a new benchmark thermal dataset 
-that comprises  over 35K distinct frames recorded, analyzed, 
-and open -sourced in challenging weather and environmental 
-conditions utilizing a low -cost yet reliable uncooled LWIR 
-thermal camera. All the YOLO v5 network variants were  
-trained using locally gathered data as well as four different 
-publicly available datasets. The performance of trained 
-networks is analysed  on both GPU as well as ARM processor -
-based edge devices for onboard automotive sensor suite 
-feasibility testing . On edge devices , the small and medium 
-network edition of YOLO is deployed and tested  due to certain 
-memory limitations and less computational power of these 
-boards . Lastly, we further optimized the smaller network 
-variant using TensorRT inference accelerator to explicitly 
-increase the FPS on edge devices. This allowed the system to 
-achieve  11 frames per second  on jetson nano , while the Nvidia 
-Jetson Xavier delivered  a significantly higher performance of  
-60 frames per second . These results  validate the potentia l for 
-thermal imaging as a core component  of ADAS systems for 
-intelligent vehicles .  
-As the future directions, the system's performance can be 
-further enhanced by porting the trained networks on more 
-advanced and powerful edge devices thus tailoring it for real -
-time onboard deployments. Moreover, the current system 
-focuses on object recognition, but it can be enhanced to 
-incorporate image segmentation, road and lane detection, traffic 
-signal and road signs classification, and object tracking  for 
-providing comprehensive driver assistanc e.  
-ACKNOWLEDGMENT  
-The authors would like to acknowledge Cosmin Rotariu from 
-Xperi -Ireland and the rest of the team members for providing 
-the support in preparing the data accusation setup and helping 
-throughout  in data collection and Quentin Noir from Lynred 
-France for giving their feedback. Moreover, the authors would 
-like to acknowledge the contributors of all the public datasets 
-for providing the image resources to carry out this research 
-work and ultralytics  for sharing the YOLO -V5 Pytorch version.  020406080100
-Small LargePerformance Comparsion of Small vs Large Model
-Model Parameters mAP Recall Precision FPS13 
- 
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- 
-Muhammad Ali Farooq  received his BE 
-degree in electronic engineering from  
-IQRA University  in 2012 and his MS 
-degree in electrical  control engineering 
-from the National University  of Sciences 
-and Technology (NUST) in 2017.   He is 
-currently pursuing the  Ph.D. degree at the 
-National University of  Ireland  Galway  
-(NUIG) . His research interests include 
-machine vision , computer vision, video analytics, and sensor 
-fusion. He has won the prestigious H2020 European Union  
-(EU)  scholarship and  currently working at NUIG as one of the 
-consortium partners in the H eliaus  (thermal v ision augmented 
-awarenes s) project  funded by EU.  
-Waseem Shariff  received  his B.E degree 
-in computer science from Nagarjuna 
-College of Engineering and Technology 
-(NCET) in 2019 and his M.S. degree in 
-computer  science,  specializing  in artificial  
-intelligence  from National  University  of 
-Ireland  Galway  (NUIG)  in 2020.  He is 
-working  as research  assistant  at National  
-University  of Ireland  Galway  (NUIG).  He 
-is associated  with Heliaus  (thermal v ision augmented 
-awarenes s) project.  He is also allied  with FotoNation/Xperi  
-14 
- 
- research  team.  His research  interests  include  machine  learning  
-utilizing  deep  neural  networks  for computer  vision  applications,  
-including  working  with synthetic  data,  thermal  data,  and RGB . 
- 
-Peter Corcoran  (Fellow, IEEE) holds a 
-Personal  Chair in Electronic Engineering at 
-the College of Science and Engineering, 
-National University of Ireland Galway  
-(NUIG) . He was the Co -Founder in several 
-start-up companies, notably FotoNation, 
-now the Imaging Division of  Xperi 
-Corporation. He has more th an 600 cited 
-technical publications and patents, more than 120 peer -
-reviewed journal articles, 160 international conference papers, 
-and a co -inventor on more than 300 granted U.S. patents. He is 
-an IEEE Fellow recognized for his contributions to digital 
-camera technologies, notably in -camera red -eye correction and 
-facial detection. He is a member of the IEEE Consumer 
-Technology Society for more than 25 years and the Founding 
-Editor of IEEE Consumer Electronics Magazine.  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 

txt/2201.02942.txt

@@ -1,1740 +0,0 @@
-See discussions, st ats, and author pr ofiles f or this public ation at : https://www .researchgate.ne t/public ation/357188680
-Fast Solver for J2-Pertu rbed Lambert Problem Using Deep Neu ral Network
-Article    in  Journal of Guidanc e, Contr ol, and Dynamics  · Dec ember 2021
-DOI: 10.2514/1.G006091
-CITATIONS
-0READS
-160
-4 author s, including:
-Some o f the author s of this public ation ar e also w orking on these r elat ed pr ojects:
-Stardust  View pr oject
-Multi-Objectiv e Hybrid Optimal Contr ol of Sp ace Syst ems View pr oject
-Bin Y ang
-Univ ersity of Str athcly de
-18 PUBLICA TIONS    57 CITATIONS    
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-Shuang Li
-Nanjing Univ ersity of Aer onautics & Astr onautics
-197 PUBLICA TIONS    1,391  CITATIONS    
-SEE PROFILE
-Massimiliano V asile
-Univ ersity of Str athcly de
-416 PUBLICA TIONS    3,755  CITATIONS    
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-All c ontent f ollo wing this p age was uplo aded b y Shuang Li  on 23 Dec ember 2021.
-The user has r equest ed enhanc ement of the do wnlo aded file.1 
-  Fast solver for J2-perturbed Lambert problem using deep 
-neural network 
-Bin Yang1 and Shuang Li2* 
-Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China 
-Jinglang Feng3 and Massimiliano Vasile4 
-University of Strathclyde, Glasgow, Scotland G1 1XJ, United Kingdom 
-This paper presents a novel and fast solver for the J2-perturbe d Lambert problem. The 
-solver consists of an intelligent initial guess generator combi ned with a differential 
-correction procedure. The intelligent initial guess generator i s a deep neural network that is 
-trained to correct the initial velocity vector coming from the solution of the unperturbed 
-Lambert problem. The differential correction module takes the i nitial guess and uses a 
-forward shooting procedure to further update the initial veloci ty and exactly meet the 
-terminal conditions. Eight sample forms are analyzed and compar e d  t o  f i n d  t h e  o p t i m u m  
-form to train the neural network on the J2-perturbed Lambert pr oblem. The accuracy and 
-performance of this novel approach will be demonstrated on a re presentative test case: the 
-solution of a multi-revolution J2-perturbed Lambert problem in the Jupiter system. We will 
-compare the performance of the proposed approach against a clas sical standard shooting 
-                                                           
-1 Ph.D. candidate, Advanced Space Technology Laboratory, No. 29 Yudao Str., Nanjing 211106, China. 
-2 Professor, Advanced Space Technology Laboratory, Email: lishua [email protected], Corresponding Author. 
-3 Assistant Professor, Department of Mechanical and Aerospace En gineering, University of Strathclyde, 75 
-Montrose Street, Glasgow, UK. 
-4 Professor, Department of Mechanical and Aerospace Engineering,  University of Strathclyde, 75 Montrose Street, 
-Glasgow, UK. 2 
- method and a homotopy-based perturbed Lambert algorithm. It wil l  be  s ho w n  t h a t, f o r  a  
-comparable level of accuracy, th e proposed method is significan tly faster than the other two. 
-I. Introduction 
-The effect of orbital perturbations, such as those coming from a non-spherical, inhomogeneous gravity field, 
-leads a spacecraft to depart from the trajectory prescribed by t h e  s o l u t i o n  o f  t h e  L a m b e r t  p r o b l e m  i n  a  s i m p l e  
-two-body model [1], [2]. Since the perturbation due to the J2 z onal harmonics has the most significant effect around 
-all planets in the solar system, a body of research exists that  addressed the problem of solving the perturbed Lambert 
-problem accounting for the J2 effect [3], [4]. This body of res earch can be classified into two categories: indirect 
-methods and shooting methods [5]. Indirect methods transform th e perturbed Lambert problem into the solution of a 
-system of parametric nonlinear algebraic equations. For instanc e, Engles and Junkins [1] proposed an indirect 
-method that uses the Kustaanheimo-Stiefel (KS) transformation t o derive a system of two nonlinear algebraic 
-equations. Der [6] presented a superior Lambert algorithm by us ing the modified iterative method of Laguerre that 
-h a s  g o o d  c o m p u t a t i o n a l  p e r f o r m a n c e  i f  g i v e n  a  g o o d  i n i t i a l  g u e s s. Armellin et al. [7] proposed two algorithms, 
-based on Differential Algebra, for the multi-revolution perturb ed Lambert problems (MRPLP). One uses homotopy 
-over the value of the perturbati on and the solution of the unpe rturbed, or Keplerian, Lambert problem as initial guess. 
-The other uses a high-order Taylor polynomial expansion to map the dependency of the terminal position on the 
-initial velocity, and solves a system of three nonlinear equati ons. A refinement step is th en added to obtain a solution 
-with the required accuracy. A common problem of indirect method s is the need for a good initial guess to solve the 
-system of nonlinear algebraic equations. A bad initial guess in creases the time to solve the algebraic system or can 
-lead to a failure of the solution procedure, especially when th e transfer time is long.  3 
- Shooting methods transcribe the perturbed Lambert problem into the search for the initial velocity vector that 
-provides the desired terminal conditions at a given time. Kraig e et al. [8] investigated the efficiency of different 
-shooting approaches and found that a straightforward differenti al correction algorithm combined with the 
-Rectangular Encke’s motion predictor is more efficient than the  analytical KS approach. Junkins and Schaub [9] 
-transformed the problem into a two-point boundary value problem  and applied Newton iteration method to solve it. 
-The main problem with shooting methods is that, with the increa se of the transfer time, the terminal conditions 
-become more sensitive to the variations of the initial velocity  and the derivatives of the final states with respect to 
-the initial velocity are more affected by the propagation of nu merical errors. In order to mitigate this problem, Arora 
-et al. [10] proposed to compute the derivatives of the initial and final velocity vectors with respect to the initial and 
-final position vectors, and the time of flight, with the state transition matrix. Woollands et al. [11] applied the KS 
-transformation and the modified Chebyshev–Picard iteration to o btain the perturbed solution starting from the 
-solution of the Keplerian Lambert problem, which is to solve th e initial velocity vector corresponding to the transfer 
-between two given points with a given time of free flight in a two-body gravitational field [12]. For the 
-multi-revolution perturbed Lambert problem with long flight tim e, Woollands et al. [13] also utilized the modified 
-Chebyshev-Picard iteration and the method of particular solutio ns based on the local-linearity, to improve the 
-computational efficiency, but its solution relies on the soluti on of the Keplerian Lambert problem as the initial 
-guesses. Alhulayil et al. [14] proposed a high-order perturbati on expansion method that accelerates convergence, 
-compared to conventional first-order Newton’s methods, but requ ires a good initial guess to guarantee convergence. 
-Yang et al. [15] developed a targeting technique using homotopy  to reduce the sensitivity of the terminal position 
-errors on the variation of the initial velocity. However, often  techniques that improve robustness of convergence by 
-reducing the sensitivity of the terminal conditions on the init ial velocity vector, incur in a higher computational cost.  4 
- The major problem of both classes of methods can be identified in the need for a judicious initial guess, often 
-better than the simple solution of the Keplerian Lambert proble m. To this end, this paper proposes a novel method 
-combining the generation of a first guess with machine learning  and a shooting method based on finite-differences. 
-We propose to train a deep neural network (DNN) to generate ini tial guesses for the solution of the J2-perturbed 
-Lambert problem and which has been a growing interest in the ap plication of machine learning (ML) to space 
-trajectory design [16], [17]. In Ref. [18] one can find a recen t survey of the application of ML to spacecraft guidance 
-dynamics and control. Deep neural network is a technology in th e field of ML, which has at least one hidden layer 
-and can be trained using a back-propagation algorithm [18]. Sán chez-Sánchez and Izzo [19] used DNNs to achieve 
-online real-time optimal control for precise landing. Li et al.  [16] used DNN to estimate the parameters of low-thrust 
-and multi-impulse trajectories in multi-target missions. Zhu an d Luo [20] proposed a rapid assessment approach of 
-low-thrust transfer trajectory using a classification multilaye r perception and a regression multilayer perception. 
-Song and Gong [21] utilized a DNN to approximate the flight tim e of the transfer trajectory with solar sail. Cheng et 
-al. [22] adopted the multi-scale deep neural network to achieve  real-time on-board trajectory optimization with 
-guaranteed convergence for optimal transfers. However, to the b est of our knowledge ML has not yet been applied 
-to improve the solution of the perturbed Lambert problem.  
-The DNN-based solver proposed in this paper was applied to the design of trajectories in the Jovian system. The 
-strong perturbation induced by the J2 harmonics of the gravity field of Jupiter creates significant differences 
-between the J2-perturbed and Keplerian Lambert solutions, even for a small number of revolutions. Hence Jupiter 
-was chosen to put the proposed DNN-based solver to the test. Th e performance of the combination of the DNN first 
-guess generation and shooting will be compared against two solv ers: one implementing the homotopy method of 
-Yang et al. [15], the other implementing a direct application o f Newton method starting from a first guess generated 5 
- with the solution of the Keplerian Lambert problem. The homotop y method in Ref. [15] was chosen for its 
-simplicity of implementation and robustness also in the case of  long transfer times. 
-The rest of this paper is organized as follows. In Sec. II, the  J2-perturbed Lambert problem and the shooting 
-method are presented. Sec. III investigates eight sample forms and their learning features for the DNN. With 
-comparative analysis of the different sample forms and standard ization technologies, the optimal sample form for 
-the J2-perturbed Lambert problem is found. The algorithm using the deep neural network and the finite 
-difference-based shooting method is proposed and implemented to  solve the J2-perturbed Lambert problem in Sec. 
-IV. Considering Jupiter’s J2 perturbation, Sec. V compares the numerical simulation results of the proposed 
-algorithm, the traditional shooting method and the method with homotopy technique. Finally, the conclusions are 
-made in Sec. VI. 
-II. J2-perturbed Lambert Problem 
-This section presents the dynamical model we used to study the J2-perturbed Lambert problem and the shooting 
-method we implemented to solve it. 
-A. Dynamical modeling with J2 perturbation 
-The J2 non-spherical term of the gravity field of planets and m oons in the solar system induces a significant 
-variation of the orbital parameters of an object orbiting those  celestial bodies. Thus, the accurate solution of the 
-Lambert problem [12] needs to account for the J2 perturbation, especially in the case of a multi-revolution transfer. 
-The dynamic equations of an object subject to the effect of J2 can be written, in Cartesian coordinates, in the 
-following form: 6 
-  2 2
-2 32
-2 2
-2 32
-2 2
-2 32311 52
-311 52
-313 52x
-y
-z
-x
-y
-zxv
-yv
-zv
-xR zvJr rr
-yR zvJr rr
-zR zvJr rr
-
-
-
-
-         
-        
-          
-
-
-
-  (1) 
-where 
-, R ,and J2 represent the gravitational constant, mean equator radius and oblateness of the celestial body, 
-respectively. ( x, y, z, vx, vy, vz) is the Cartesian coordinates of the state of the spacecraft, and 22 2rx y z   i s  
-the distance from the spacecraft to the center of the celestial  body. 
-B. Shooting Method for the J2-perturbed Lambert Problem 
-The classical Lambert problem (or Keplerian Lambert problem in the following) considers only an unperturbed 
-two-body dynamics [12]. However, perturbations can induce a sig nificant deviation of the actual trajectory from the 
-solution of the Keplerian Lambert problem. One way to take pert urbations into account is to propagate the dynamics 
-in Eqs. (1) and use a standard shooting method for the solution  of two-point boundary value problems.  
-Fig. 1 depicts the problem introduced by orbit perturbations. T he solution of the Keplerian Lambert problem, 
-dashed line, provides an initial velocity v0. Because of the dynamics in Eq.(1), the velocity v0 corresponds to a 
-difference f0 f f0rr r  between the desired terminal position fr and the propagated one f0r, when the dynamics 
-is integrated forward in time, for a period tof, from the initial conditions [ r0, v0]. In order to eliminate this error, one 
-can use a shooting method to calculate a velocity v that corrects v0. Fig. 1 shows an example with two subsequent 
-varied velocity vectors vi and the corresponding terminal conditions. 7 
- 
-0rfr
-0r
-ir0viv0v
-iv
-nvf0r
-fir
- 
-Fig. 1 Illustration of the shooti ng method based on Newton’s it eration algorithm for the J2-perturbed 
-Lambert problem  
-As mentioned in the introduction, shooting methods have been ex tensively applied to solve the perturbed 
-Lambert problem. Different algorithms have been proposed in the  literature to improve both computational 
-efficiency and convergence, e.g. the Picard iteration [11] and the Newton’s iteration [23]. In this section, the 
-standard shooting method based on Newton’s algorithm is present ed [23]. Given the terminal position rfi = [xi, yi, zi]T 
-and the initial velocity vi = [vxi, vyi, vzi]T at the i-th iteration, the shooting method requires the Jacobian matrix : 
- =iii
-xiy iz i
-iii
-i
-xiy iz i
-iii
-xiy iz ix xx
-vvv
-y yy
-vvv
-zzz
-vvv    
-  
- 
-  
-   H , (2) 
-to compute the correction term: 
- 1
-f iivHr r , (3) 
-where J-1 is the inverse of the Jacobian matrix Hi, and rf is the desired terminal position, as shown in Fig. 1. The 
-corrected initial velocity then becomes 1ii i vvv . 8 
- Here the partial derivatives in the Jacobian matrix are approxi mated with forward differences. Finite differences 
-are computed by introducing a variation 610v in the three components of the initial velocity and computing t he 
-corresponding variation of the three components of the terminal  conditions ixr, iyr, and izr. Consequently, the 
-Jacobian matrix can be written as follows. 
- =iy ix iz
-ivvv
- 
- 
- r rrH   (4) 
-Because of the need to compute the Jacobian matrix in Eq. (2), finite-difference-based shooting methods need to 
-perform at least three integrations for each iteration. Further more, if the accuracy of the calculation of the Jacobian 
-matrix in Eq.(2) is limited, this algorithm could fail to conve rge to the specified accuracy or diverge, which is a 
-common situation if the time of flight is long (e.g., tens of r evolutions). Homotopy techniques are an effective way 
-to improve the convergence of standard shooting methods for MRP LP but still require an initial guess to initiate the 
-homotopy process and can require the solution of multiple two-p oint boundary value problems over a number of 
-iterations. Here a DNN is employed to globally map the change i n the initial velocity to the variation of the terminal 
-position for a variety of initial state vectors and transfer ti mes. This mapping allows one to generate a first guess for 
-the initial velocity change ivby simply passing the required initial state, transfer time and  terminal condition as 
-input to the DNN.  
-In the following, we will present how we trained the DNN to gen erate good first guesses to initiate a standard 
-shooting method. We will show that an appropriately trained DNN  can generate initial guesses that provide 
-improved convergence of the shooting method even for multi-revo lution trajectories. It will be shown that the use of 
-this initial guess improves the robustness of convergence of a standard shooting method and makes it significantly 
-faster than the homotopy method in [15]. 9 
- III. Sample Learning Feature Analysis 
-DNN consists of multiple layers of neurons with a specific arch itecture, which is an analytical mapping from 
-inputs to outputs once its parameters are given. The typical st ructure of DNN and its neuron computation is 
-illustrated in Fig. 2. The output of each neuron is generated f rom the input vector x, the weights of each component 
-w, the offset value b, and the activation function y=f(x). The inputs are provided according to the specific problem or  
-the outputs of the neurons of the previous layer. The weight an d offset values are obtained through the sample 
-training. The activation function is fixed once the network is built. The training process includes two steps: the 
-forward propagation of the input from the input layer to the ou tput layer; and then the back propagation of the output 
-error from the output layer to the input layer. During this pro cess, the weight and the offset between adjacent layers 
-are adjusted or trained to reduce the error of the outputs.  
-ii sbw x
-  yf s
- 
-Fig. 2 The diagram of the DNN s tructure and neuron computation 
-The ability of a DNN to return a good initial guess depends hig hly on the representation and quality of samples 
-used to train the network. High-quality samples cannot only imp rove the output accuracy of the network, but also 10 
- reduce the training cost. Therefore, in the following, we prese nt the procedure used to generate samples with the 
-appropriate features. 
-A. Definition of Sample Form and Features  
-In this work two groups of sample forms have been considered: o ne has the initial velocity v0 solving the 
-J2-perturbed Lambert problem as output, the other has the veloc ity correction 0v to an initial guess of v0 a s  
-output.  
-For the first group of sample forms, the input to the neural ne twork includes the known initial and terminal 
-positions 0f,rr and the time of flight tof. The output is only the initial velocity v0 as the terminal velocity can be 
-obtained through orbital propagation once the initial velocity is solved. This type of sample form is defined as 
-    0f 0,, ,vSt o frr v   (5) 
-where the subscript 0 and f denotes the start and end of the tr ansfer trajectory, respectively. Thus, when trained with 
-sample form in Eq. (5), the DNN is used to build a functional r elationship between 0f,,tof rr and 0v. 
-The second group of sample forms was further divided in two sub groups. One that uses the initial state of the 
-spacecraft 0r, the time of flight tofand the terminal error fras input and the other that uses the initial state 0r, 
-the time of flight tof, the terminal position error frand the initial velocity vector from the Keplerian solution 
-dvas inputs. These two sample forms are defined as follows:  
-   
-  d1 0 f 0
-d2 0 d f 0,, ,
-,, , ,v
-vSt o f
-St o f 
- rr v
-rv r v  (6) 
-In Eq. (6) the output 0v is always the initial velocity correction 00 dvv v , in which 0v is the initial 
-velocity that solves the J2-perturbed Lambert problem. Thus, wh en trained with sample forms Sdv1 and Sdv2, the DNN 11 
- realizes a mapping between 0v a n d  0f,,tof rr or  0d f,, ,tof rv r respectively. The difference between Sdv1 
-and Sdv2 is whether the input includes the initial velocity vd t h a t  i s  n e c e s s a r y  f o r  s o l v i n g  t h e  J a c o b i a n  m a t r i x .  
-Therefore, it is theoretically easier to obtain the desired map ping with the input including the initial velocity, i.e. Sdv2. 
-However, this increases the dimensionality of the sample and mi ght increase the difficulty of training. 
-For each group of sample forms there are three main ways of par ameterizing the state of the spacecraft: Cartesian 
-coordinates, spherical coordinates and the mean orbital element s. Cartesian coordinates provide a general and 
-straightforward way to describe the motion of a spacecraft but state variables change significantly over time even for 
-circular orbits with no orbital perturbations. Spherical coordi nates can provide a more contained and simpler 
-variation of the state variables but are singular at the poles.  Double averaged mean orbital elements present no 
-variation of semimajor axis, eccentric and inclination due to J 2 and a constant variation of argument of the perigee 
-and right ascension of the ascending node [24]. Which parameter ization to choose for the training of the DNN will 
-be established in the remainder of this section. The structures  of Eqs. (5) and (6) expressed in terms of these three 
-coordinate systems are as follows: 
-  
-  
-  
-
-Car 0 0 0 f f f 0 0 0
-S p h 0 0 0 fff 0 0 0
-OEm 0 f 0 0 0
-d1 C a r 0 0 0 f f f 0 0 0d1 S p h 0 0 0 f f f,,,,,, , ,
-,,, ,,, , ,
-,, ,,
-,,, , , , , , ,
-,,, , , ,vx y z
-vv v
-vv v
-vx y zvSx y z x y z t o f v v v
-Srrt o f v
-So e o e t o f v
-Sx y z x y z t o f v v v
-Srr t o f v   
-
-  
-
-
- 
-
-
-      
-   ，
-，
-，
-， 
-
-  
-  00 0
-d2 C a r 0 0 0 d d d f f f 0 0 0
-d2S p h 0 0 0 d d d f f f 0 0 0
-d2 O E m d f f f 0 0 0,,
-,,, , , , , , , , , ,
-,,, ,,, , , , , ,
-,, , , , ,vv
-vx y z x y z
-v vv
-vv vSx y z v v v x y z t o f v v v
-Sr vr t o f v
-So e r t o f v
-     
-  
-
-
-         
-     
-     ，
-，  (7) 
-where the subscript Car, Sph and OEm  denote the Cartesian coordinate, the spherical coordinate and mean orbital 
-elements, respectively. And x, y, and z are the Cartesian coordinates of the position vector. And r, , and  a r e  12 
- the distance, azimuth, and elevation angle of position vector i n the spherical coordinate system. 
-,, , , ,Toe a e i w M  represents the mean orbital elements. 
-B. Performance Analysis of Different Sample Forms 
-In this section the performance of the eight sample forms defin ed in Eq.(7) is assessed in order to identify the 
-best one to train the DNN. We always generate a value for the i nitial conditions starting from an initial set of orbital 
-elements. Values of the orbital parameters for each sample are randomly generated with the rand  function in 
-MATLAB using a uniform distribution over the intervals defined in Table 1. Note that semimajor axis and 
-eccentricity are derived from the radii of the perijove and apo jove. Considering the strong radiation environment of 
-Jupiter and the distribution of Galilean moons, we want to limi t the radius of the pericentre rp of the initial orbit of 
-each sample to be in the interval [5 RJ, 30RJ], where RJ = 71492 km is the Jovian mean radius. The value of the 
-inclination is set to range in the interval [0, 1] radians. The  time of flight does not exceed one orbital period T, which 
-is approximately calculated using the following formula 
- 3
-J=2aT  (8) 
-where a is the semi-major axis, a = ( ra + rp ) / 2. 
-Table 1 Parameters’ ranges of the sample 
-Parameters Range 
-Apojove radius ra (×RJ) [ rp, 30] 
-Perijove radius rp (×RJ) [5, 30] 
-inclination (rad) [0, 1] 
-RAAN (rad) [0, 2) 
-Argument of perigee (rad) [0, 2) 
-Mean anomaly (rad) [0, 2) 
-tof (T) (0, 1) 13 
- The following procedure is proposed to efficiently generate a l arge number of samples without solving the 
-J2-perturbed Lambert problem: 
-Step 1: The initial state [ r0, v0] and time of flight tof are randomly generated. 
-Step 2: The terminal state [ rf, vf] is obtained by propagating the initial state [ r0, v0] under the J2 perturbation 
-dynamics model, for the propagation period tof. 
-Step 3: The Keplerian solution vd is solved from the classical Lambert problem with the initial and terminal 
-position r0, rf and flight time tof. 
-Step 4: The end state [ rfd, vfd] is obtained by propagating the initial Keplerian state [ r0, vd] under the J2 
-perturbation dynamics model, and for the propagation period tof. 
-Step 5: The initial velocity correction 0v and the end state error fr are computed with00 dvv v  and 
-ff f drr r . 
-Using these five steps, we generated 100000 samples and then gr ouped them in the eight sample forms given in 
-Eq.(7). Before training, a preliminary learning feature analysi s is performed on the distribution of sample data and 
-the correlation between the inputs and the output. Specifically , the mean, standard deviation, and magnitude 
-difference coefficients are used to describe the distribution o f the data, and the Pearson correlation coefficient is 
-chosen to evaluate the correlation of the data. Their mathemati cal definitions are given as follows 
- 
-
-1
-2
-1=
-1
-max
-log
-min 0n
-j
-j
-n
-j
-jX
-Xn
-XXn
-X
-X
-
-
-
-
-   (9) 14 
- where X and  are the mean and standard deviation of the data, respectively.  And n is the total number of data. 
- denotes the magnitude difference coefficients that assesses th e internal diversity of the data.  
-The statistical characteristics of the variables in the sample are given in Table 2. For the variables described in 
-Cartesian coordinate, the mean values are close to 0 but the st andard deviations are generally large. Furthermore, 
-their magnitude difference coefficients are all more than 5, wh ich indicate a large difference in the absolute values 
-of the variables. For the variables described in spherical coor dinate, the most of their standard deviations are less 
-than these described in the Cartesian coordinate. In addition, the magnitude difference coefficients of the magnitude 
-of the position and velocity vectors are less than 1. The varia bles with smaller standard deviation have better 
-performance in the training process. Therefore, the samples wit h the variables represented in spherical coordinate 
-are easier to learn than those described in Cartesian coordinat es.  
-Table 2 The statistical distributi ons of the variables in the s amples  
-Parameters 
-of sample Mean Standard deviations Magnitude difference 
-coefficients 
-r0-Car [-0.014; 0.087; 0.001] [11.424; 11.424; 1.145] [5.125; 5.949; 7.077] 
-r0-Sph [14.954; 0.007229; 0.000193] [6.221; 1.815; 0.070] [0.777; 4.9 26; 6.607] 
-rf-Car [0.001; -0.031; -0.005] [12.438; 12.469; 1.246] [5.094; 4.371;  6.442] 
-rf-Sph [16.503; -0.005966; -0.000386] [6.275; 1.813; 0.070] [0.777; 4 .454; 6.321] 
-v0-Car [-0.088351; -0.032116; 
--0.006130] [8.916; 8.887; 0.895] [5.450; 5.185; 6.338] 
-v0-Sph [12.082577; -0.002034; 
--0.000529] [3.647; 1.821; 0.071] [0.773; 5.257; 5.851] 
-oe0 [15.771; 0.257895; 0.087045; 
-3.148016;  3.137227; 3.138286] [5.225; 0.177; 0.050; 
-1.813;1.812;1.816] [0.774; 5.273; 4.145; 
-4.653; 5.422; 4.634] 
-oef [15.771; 0.257850; 0.087045; 
-3.147935; 3.137600; 3.151625] [5.225; 0.177; 0.050; 
-1.813; 1.812; 1.528] [0.774; 4.721; 4.145; 
-5.917; 5.228; 5.163] 
-vd-Car [-0.087568; -0.031006; 
--0.006340] [8.915; 8.886; 0.895] [5.654; 5.578; 6.673] 
-vd-Sph [12.081729; -0.001599; 
--0.000538] [3.647; 1.821; 0.071] [0.774; 5.651; 6.658] 
-f-Carr [-3.162; -11.384; -0.075] [1369.838; 1395.080; 
-187.322] [10.495; 10.828; 11.371]15 
- f-Sphr  [1154.249; -0.004; -0.001] [1589.222; 1.817; 0.135] [10.283; 4. 831; 8.576] 
-oed [15.769; 0.258264; 0.087096; 
-3.147709; 3.136805; 3.139263] [5.226179; 0.177; 0.051; 
-1.813; 1.812; 1.814] [9.556; 4.800; 5.049; 
-5.240; 5.927; 4.639] 
-tof 4.023 3.220 5.481 
-0-Carv  [-0.000782; -0.001109; 0.000210] [0.326; 0.283; 0.063] [9.917; 1 0.432; 10.471]
-0-Sphv  [0.013321; 0.003913; 0.002884] [0.436; 1.818; 0.503] [8.948; 4. 672; 5.924] 
-It is also known that the learning process is easier if the cor relation between the input and output of the sample is 
-stronger. Here the Pearson correlation coefficient is used to d escribe this correlation and is defined as follows  
- 
-1n
-jj
-j
-XYXX Y Y
-R
-
- ( 1 0 )  
-where  n is the total number of sample data. Y and Yrepresent the mean and standard deviation of the data Y. 
-X a n d  Xdenote the mean and standard deviation of the data X. 
-The matrix of the Pearson correlation coefficients of the propo sed sample’s inputs and outputs are given in Table 
-3. The elements of Pearson correlation coefficients matrix are the correlation coefficient between the corresponding 
-input and output variables. The signs of the elements indicate positive and negative correlations, respectively. The 
-absolute values of elements represent the strength of correlati on. The greater the absolute value is, the stronger the 
-correlation is.  
-Table 3 The matrix of the Pearson correlation coefficients of t he input and output for different sample forms 
-Sample 
-Forms Pearson correlation coefficients matrix 
-Sv-Car 0.003 0.004 0.000 0.002 0.002
-0.002 0.003 0.001 0.001 0.003
-0.005 0.000 0.002 0.001 0.002 0.000 0.002 
-
-  
-
--0.764 0.126
-0.764 -0.122  
-Sv-Sph 0.005 0.001 0.003 0.000
-0.002 0.000 0.004 0.000 0.001 0.003
-0.001 0.001 0.002 0.003 0.002 0.001 0.003
-
-  
-
--0.898 -0.459 -0.344
--0.116  
-Sv-OEm 0.002 0.002 0.002 0.000 0.002 0.003 0.002 0.001
-0.002 0.000 0.000 0.002 0.003 0.007 0.002 0.000 0.000 0.002 0.003 0.001 0.0 03
-0.002 0.001 0.007 0.004 0.002 0.001 0.002 0.001 0.007 0.004 0.000 
- -0.587 0.291 -0.587 0.291 -0.344
-0.008 0.003
-
-16 
- Sdv1-Car 0.006 0.002 0.002
-0.004 0.000 0.002
-0.003 0.006 0.003 0.004 0.004
- 
-
-
--0.011 -0.049 -0.053 -0.046
-0.010 0.037 -0.041 -0.013
-0.011 -0.090 
-Sdv1-Sph 0.003 0.005 0.004 0.002
-0.002 0.000 0.002 0.000 0.001
-0.001 0.002 0.001 0.001 0.004
-
-
-
--0.025 0.081 0.010
-0.377 0.254
-0.512 0.045 
-Sdv2-Car 0.006 0.002 0.000 0.002
-0.004 0.000 0.002 0.002
-0.003 0.006 0.003 0.004 0.004 0.004
-  
-
-
--0.011 -0.017 -0.014 -0.049 -0.053 -0.046
-0.010 0.011 -0.012 0.037 -0.041 -0.013
-0.010 -0.040 0.011 -0.090 
-Sdv2-Sph - . 0.003 -0.005 . 0.004 -0.005 . 0.004 -0.002 .
--0.002 . 0.000 0.005 . -0.001 0.002 . 0.000 -0.001
--0.001 -0.002 . 0.003 0.004 . 0.001 -0.001 . 0.004      
-      
-            0 025 0 032 0 081 0 010
-0 377 0 259 0 254
-05 1 2 02 9 7 00 4 5
-Sdv2-OEm  0.001 0.004 0.001 0.004 0.002
-0.002 -0.003 0.002 0.004 0.002 0.001 0.002 0.000 0.001
-0.000 0.001 0.002 0.003 0.002 0.001 0.001 0.004 
-
- 
-
--0.019 0.082 0.076 0.081 0.010
-0.254
-0.010 0.045 
-First, it is seen that most elements of the matrix are less tha n 0.01, indicating the correlations between the inputs 
-and the outputs are generally weak. Second, for the first three  sample forms of Table 3, the absolute values of all 
-elements for some rows are less than 0.01. This means that some  components of the output variable are in 
-weak-correlation with all input variables, and hence the mappin g from these output components to the input 
-variables is very difficult to capture. Therefore, samples with  the initial velocity as output, i.e. Sv-Car, Sv-Sph, and 
-Sv-OEm, are not deemed to be ideal for the training of the neural net work. Third, by comparing the matrix listed in 
-rows 4 to 7 of Table 3, the absolute values of the elements for  the samples described in Cartesian coordinates are 
-smaller than those for the samples described in spherical coord inates. Furthermore, for the samples in spherical 
-coordinates, it is seen that the submatrix of each input variab le in the Pearson correlation coefficients matrix is a 
-diagonally dominant matrix, where the elements with large absol ute values for each input variable are distributed in 
-different rows and columns, and are independent. Therefore, the  samples described in the spherical coordinate have 
-better learning features and performance due to the strong corr elations. Additionally, for Sdv2-Sph that includes the 
-Keplerian solution vd as one of the inputs, the correlation with the initial velocit y correction 0v i s  [0.032 , 0.004, 17 
- -0.005; 0.005, 0.259 , -0.001; 0.003, 0.004, 0.297 ], which is diagonally dominant with large diagonal values, whi ch 
-demonstrates that the Keplerian solution is an important input.  Finally, for the sample in the mean orbital elements 
-in the last row of Table 3, the matrix only contains a few elem ents whose absolute values are greater than 0.01, and 
-most of them are distributed in the first row. The mean variati ons of semimajor axis, eccentricity and inclination are 
-not affected by the J2 perturbation but only by the variation o f the initial velocity. Therefore, only the first row in the 
-matrix displays larger values. In addition, the elements in the  first six columns of the Pearson correlation matrix of 
-Sdv2-OEm  are generally smaller than others in Table 3, because the outp uts of the sample is the initial velocity 
-correction, which is calculated using the osculating orbital el ements that contain both the long and the short term 
-effects of the J2 perturbation. Thus the correlation using the mean orbital elements is moderate. This would suggest 
-that the sample Sdv2-Sph is the best option for the training of the DNN among the eight  tested sample forms. We will 
-now quantify the training performance for each of the eight sam ple forms by comparing the training convergence of 
-a given DNN. It has to be noted that the structure of the DNN p lays a role as well. For example, a high dimensional 
-sample with more variables needs a larger size DNN with more la yers and neurons. However, we argue that, since 
-the sample form selection mainly depends on the problem and the  dynamics, a better sample form will have better 
-training performance than other sample forms given the same DNN  structure. For this reason, it is reasonable to 
-compare sample forms even on DNN structures that are not optima l. The effect of the structure of DNN on the 
-training performance will be discussed in section V. 
-Some data pretreatment is necessary to facilitate the training process and improve the prediction accuracy. 
-Standardization, normalization and logarithms are used to pre-p rocess data with large ranges or magnitude 
-differences. Tests in this section were performed using a four- layer fully connected DNN with 50 neurons per 
-hidden layer. The activation functions of the hidden layers and  the output layer are all Tanh. The Adaptive moment  estimatio n
-algorith m
-The con s
-training p
-output o f 
- 
-where n i
-Here MS E
-From 
-significa n
-has a lar gn (Adam) [25 ]
-m works throu g
-struction and 
-process, the va
-fthe sample f o
-s the number o
-E has no unit s
-Fig. 3 one c a
-ntly smaller t h
-ger range of v] was employ e
-gh the entire t
-training of t h
-ariations of t h
-or different sa m
-of samples, a n
-s because data 
-Fig. 3 The tr
-an see that th e
-han that with t h
-values and the r
-ed for the opt i
-training datas e
-he DNN are b
-he mean squa r
-mple forms ar e
-MSE
-ndˆiyand y i are 
-has been nor m
-ainin g conve r
-e MSE of the n
-he initial velo c
-refore has a m
-imization. Th e
-et) was set to 
-based on the 
-re error (MS E
-e given in Fig
-
-11ˆn
-i
-iE y
-n
-the output pr e
-malized befor e
-rgence histor y
-neural netwo r
-city as the ou t
-more scattere d
-e maximum e p
-10000 and t h
-Python impl e
-E) between th e
-. 3. The math e
-2
-iy
-edicted by the 
-e training. 
-y for differe n
-rk with the in i
-tput. This is b e
-d distribution. 
-poch (or num b
-he initial lear n
-ementation o f 
-e output of t h
-ematical expr e
-DNN and th e
- 
-nt sample for m
-itial velocity c
-ecause the ini t
-Also, the M S
-ber times that 
-ning rate was s
-fTensorFlow. 
-he neural net w
-ession of the M
-e true output r e
-ms 
-correction as t
-tial velocity i n
-SE of sample S
-18the learning 
-set to 0.001. 
-During the 
-work and the 
-MSE is: 
-(11)
-espectively . 
-the output is 
-n the sample 
-Sdv1-Sph is an 19 
- order of magnitude higher than that of sample Sdv2-Sph. Therefore, the accuracy of predicting the initial velocity 
-correction is effectively improved by including the Keplerian v elocity in the input of the sample. The blue line in 
-Fig. 3 has obvious fluctuations due to the weak correlations be tween the output and the input of Sv-OEm, as shown in 
-Table 3. Finally, the training results of the samples in spheri cal coordinate are better than those in Cartesian 
-coordinates, which is consistent with the conclusions drawn in previous sections. 
-In summary, for the J2-perturbed Lambert problem, the samples d escribed in spherical coordinate appear to be 
-more suitable for the training of a DNN. In fact, among all eig ht sample forms, the sample form Sdv2-Sph yielded the 
-best learning converge, given the initial position, Keplerian v elocity, the terminal position error of the Keplerian 
-solution and time of flight as inputs and the initial velocity correction as output. Therefore, in the remainder of this 
-paper, the Sdv2-Sph sample form is selected for the training of the DNN.  
-IV. Solution of the J2-perturbed La mbert Problem Using DNN 
-The proposed solution algorithm (see the flow diagram in Fig. 4 ) is made of an Intelligent initial Guess 
-Generator (IGG) and a Shooting Correction Module (SCM). The DNN  is used in the IGG to estimate the correction 
-of the Keplerian solution and provide an initial guess to the s hooting module. The shooting method discussed in part 
-B of Section II is employed in th e SCM to converge to the requi red accuracy.   As s h
-vectors. T
-terminal p
-form an d
-shooting 
-rendezvo u
-approxi m
-The m
-terminal s
-one call t o
-method mFig. 4
-hown in Fig. 4
-Then the init i
-position error 
-d the generat i
-method in S e
-us constraint .
-mated with the 
-method propo s
-state, where  i 
-o the DNN ar e
-mainly depend4 The flow c h
-4, first the K e
-ial conditions 
-rfd. With th i
-ion method o
-ection II is a
-. The Jacobi a
-difference qu o
-sed here perfo r
-is the numbe r
-e necessary t o
-s on the SC M
-hart of the pr o
-eplerian Lam b
-[r0, vd] are p
-is erro r, the i n
-of the sample s
-applied to co r
-an matrix is 
-otient to redu c
-rms a total of 
-r of iterations.
-o obtain the in i
-M. As it will be 
-oposed J2-pe
-bert problem 
-propagated f o
-nitial velocity 
-s a r e  d e s c r i b e
-rrect the initi a
-calculated a c
-ce the comput a
-4i+2 numeric
- Additionally ,
-itial velocity g
-shown in the 
-rturbed La m
-is solved wit
-orward in tim e
-correction is 
-ed in Sectio n
-al velocity to
-ccording to E
-ational load. 
-al propagatio n
-, one solution 
-guess. Theref o
-next section, 
- 
-mbert proble m
-th the desired 
-e u n d e r  t h e  e
-calculated us i
-n I I I .  T h e n  t h
-make the te r
-Eq.(4), where 
-ns to obtain t h
-of the Keple r
-ore, the calcul a
-the initial gu e
-m solver 
-initial and f i
-ef f e c t  o f  J 2  t o
-ing the traine d
-he finite diff e
-rminal positi o
-the partial d
-he Jacobian ma
-rian Lambert p
-ation time of t
-ess provided b y
-20inal position 
-o obtain the 
-d DNN. The 
-erence-based 
-on meet the 
-derivative is 
-atrix and the 
-problem and 
-the proposed 
-y the IGG is 21 
- close enough to the final solution that the number of iteration s required to the SCM to converge to the required 
-accuracy is significantly reduced. 
-V. Case Study of Jupiter Scenario 
-In this section, taking the Jovian system as an example, some n umerical simulations are performed to 
-demonstrate the effectiveness and efficiency of the proposed J2 -perturbed Lambert solver. Firstly, different network 
-structures and training parameters are tested to find the optim al ones for this application. Then, we simulate the 
-typical use of the proposed solver with a Monte Carlo simulatio n whereby a series of transfer trajectories are 
-computed starting from a random set of boundary conditions and transfer times. To be noted that although the tests 
-in this section use the J2, μ and R, of Jupiter the proposed method can be generalized to other ce lestial bodies by 
-training the corresponding DNNs with a different triplet of val ues J2, μ and R, but using the same sample form. 
-A. DNN Structure Selection and Training 
-With reference to the results in Section III, the samples used to train the DNN include the initial position, the 
-initial velocity, coming from the solution of the Keplerian Lam bert problem, the terminal position error of the 
-Keplerian solution, and the time of flight. The output is the i nitial velocity correction of the Keplerian solution and 
-all vectors in a sample are expressed in spherical coordinates.  In order to generalize the applicability of this method, 
-the ranges of the parameters of the sample given in Table 1 hav e been appropriately expanded. The range of orbital 
-inclinations is [0, ] in radian. The range of times of flight is now in the open in terval (0, 10 T), where T is calculated 
-using Eq. (8) from the initial state ( r0, v0). The ranges of other parameters are consistent with Table 1. In total, 
-200000 training samples are obtained using the rapid sample gen eration algorithm given in part B of Section III.  Since 
-results, i n
-one woul
-sample f o
-We s t
-learning w
-and ReL U
-The sphe[-0.5
-, 0
-of the th r
-chosen a s
-Also i
-other trai n
-in Table 4the structure 
-n this section 
-d need to loo p
-orm remains r e
-tart by defini n
-while Sigmoi d
-U will be use d
-rical coordin a
-0.5]. Becau s
-ree compone n
-s the activatio n
-in this case t h
-ning paramet e
-4. and training p
-we analyze d
-p back and ch e
-easonably go o
-ng the activa t
-d functions ar e
-d. The output r
-ates (magnitu d
-se the range o
-nts of the sph e
-n function of t
-Fig
-he Adaptive m
-ers are the sa m
-parameters o f 
-different DNN 
-eck the optim a
-od even once t h
-tion functions
-e less used bec
-ranges of Tan h
-de, azimuth, a
-f elevation a n
-erical coordin
-the output lay e
-g. 5 The t ypica
-moment estim a
-me as in Secti o
-fthe neural ne
-structures a n
-ality of the sa m
-he DNN stru c
-. Tanh and R
-cause the gra d
-h and ReLU a
-and elevation) 
-ngle can be tr a
-ates can all m
-er.  
-al activation 
-ation is used a
-on III. The tr a
-twork also pl a
-nd settings. N o
-mple form, ho
-cture is chang e
-ReLU are the 
-dient tends to 
-are [-1, 1] and 
-of the outpu t
-ansformed fro m
-meet the requi
-functions for 
-as optimizer. T
-aining results 
-ays a signific a
-ote that once t
-wever, in this 
-ed.  
-common acti v
-vanish [26], t h
-[0, ∞] respec t
-t of the samp l
-m [-0.5, 0.5
-rements of R e
- 
- DNN  
-The maximu m
-of DNNs wit h
-ant impact on 
-the structure i
- paper we ass u
-vation functi o
-hus in the fol l
-tively, as sho w
-le are [0, ∞], 
-5] to [0, ]
-eLU. Therefo
-m epoch is 5 0
-h different si z
-22the training 
-is optimized 
-ume that the 
-on s  f o r  d e e p  
-lowing Tanh 
-wn in Fig. 5. 
-[0, 2] and 
-], the ranges 
-re, ReLU is 
-0000 and the 
-zes are listed 23 
- Table 4 Training results of DNNs with different sizes 
-Hidden 
-Layers Neurons per 
-hidden layer activation 
-function MSE Training 
-time (s) 
-2 20 ReLU 9.423e-05 762 
-Tanh 3.286e-05 839 
-50 ReLU 1.435e-05 951 
-Tanh 1.226e-05 1084 
-100 ReLU 9.423e-06 1210 
-Tanh 9.163e-06 1425 
-3 20 ReLU 2.423e-06 1198 
-Tanh 2.154e-06 1267 
-50 ReLU 1.315e-06 1347 
-Tanh 1.258e-06 1523 
-100 ReLU 5.631e-06 1746 
-Tanh 1.226e-06 1935 
-4 20 ReLU 9.423e-06 1648 
-Tanh 3.286e-06 1864 
-50 ReLU 7.522e-07 1977 
-Tanh 4.816e-07 2186 
-100 ReLU 6.395e-05 2361 
-Tanh 2.861e-05 2643 
-The neural network with the minimum MSE has 4 hidden layers, ea ch with 50 neurons. The activation function 
-of its hidden layers is Tanh. Additionally, some conclusions ca n be made from Table 4. Firstly, the networks with 
-ReLU as the activation function take less time for training. Se condly, the networks with Tanh as the activation 
-function achieve smaller MSEs. Thirdly, the network with 4 hidd en layers and 100 neurons in each hidden layer has 
-overfitted during the training process. 
-The variation of MSE of the neural network with 4 hidden layers  and with 50 neurons for each layer is shown in 
-Fig. 6. MSE finally converges to 4.816e-07, which transforms in to the mean absolute error (MAE) of the DNN’s 
-output: [0.004241 km/s; 0.000232 rad; 0.000152 rad].   In or d
-trained s a
-of the tr a
-terminal p
-of the tra i
- 
-Fig. 7
-DNN. [Δ v
-Kepleria n
-the termi n
-points in 
-also redu c
-After the der to verify t
-amples were r
-ained DNN. T
-position rf are 
-ined DNN ( vc,
-7 and Fig. 8 s
-v0dx; Δv0dy; Δv
-n solutions, re s
-nal position a
-Fig. 7 and Fi g
-ced significa n
-correction b yFig. 6 M S
-the predictio n
-randomly reg e
-The initial vel o
-used as refer e
-, rfc) are calcu
-show the co m
-v0dz] and [Δ rfdx
-spectively. [ Δ
-after the DN N
-g. 8) is much c
-ntly after the c
-y the DNN, th
-SE of the sele c
-n accuracy of 
-enerated with t
-ocity v0, whi c
-ence values. T
-lated as follo w
-0d
-0cv
-v
-mparison betw e
-x; Δrfdy; Δrfdz]
-Δv0cx; Δv0cy; Δv
-N’s correc tion
-closer to 0 aft e
-correction, wh
-e initial velo c
-cted DNN du r
-the trained D
-the algorithm 
-ch is the exac t
-The errors of t h
-ws 
-0df d
-0cf c,
-,
-vv r
-vvr
-een the Kepl e
-are the errors
-v0cz] and [Δ rfcx
-s, respectivel y
-er the DNN’s c
-ich is indicat e
-city error is li m
-ring the trai n
-DNN, 1000 n e
-in part B of S
-t solution of t
-he Keplerian s
-ff d
-ff c
-rr
-rr
-erian solution s
-of the initial v
-x; Δrfcy; Δrfcz]
-y. It can be s
-correction. T h
-ed by the len g
-mited to 10 m
- 
-ning process. 
-ew samples t h
-Section III to 
-the J2-pertur b
-solutions ( vd, r
-s and the app r
-velocity and t h
-are the errors 
-een that the m
-he standard de v
-gth of the blue 
-m/s, and the te r
-hat are differ e
-examine the p
-bed Lambert p
-rfd) and the ap p
-roximation o f
-he terminal p o
-of the initial v
-mean of thes e
-viation of the s
-bars in Fig. 7
-rminal positio n
-24ent fro m t h e  
-performance 
-proble m an d 
-proximation 
-(12)
-f t h e  t r a i n e d  
-osition of the 
-velocity and 
-e errors (red 
-se errors has 
-7 and Fig. 8. 
-n error does  not exce e
-initial va l
-Fig. 7 T
-Fig.
-B. Perfo
-In this s e
-method ued 100 km. T
-lue with respe c
-The statistica l
-. 8 The statis t
-ormance Ana l
-ection the pr o
-using NewtonThis proves th a
-ct to a simple 
-l results of th e
-tical results o
-lysis for MR P
-oposed DNN- b
-’s iteration a l
-at the applic a
-Keplerian La m
-e initial velo c
-of the termin a
-PLP  
-based metho d
-lgorithm (SN )
-ation of the D
-mbert solutio n
-city errors of t
-al position er r
-correctio n
-d is compare d
-) a n d  t h e  h o m
-DNN has sign i
-n. 
-the Kepleria n
-rors of the K e
-n  
-d a g a i n s t  o t h e
-motopic pertu r
-ificantly imp r
- 
-n solution an d
-eplerian solu t
-er two metho d
-rbed Lamber t
-roved the acc u
-d the DNN’s c
-tion and the D
-ds: a traditio n
-t algorith m (H
-25uracy of the 
-correction 
-DNN’s 
-nal shooting 
-HL) in [15]. 26 
- When applying the HL, the C++ version of Vinit6 algorithm in li terature [27] is employed to implement the HL 
-method in Ref. [15] and to decrease the CPU computation time of  HL. The HL is running in Matlab and the MEX 
-function calls the Vinit6 algorithm that is running in visual s tudio 2015 C++ compiler to analytically propagate the 
-perturbed trajectory. The accuracy tolerance of Vinit6 algorith m is set at 1 10-12. The homotopy parameter is 
-defined as the deviation in the terminal position and other det ails of implementation and settings are the same as 
-these given in Ref. [15]. For the SN and the proposed method, t heir dynamical models only include the J2 
-perturbation. For the Vinit6 algorithm, the dynamical model inc ludes the J2, J3 and partial J4 perturbations. 
-However, the magnitudes of J3 and J4 of Jupiter are much smalle r than that of J2. Their perturbation effects are very 
-weak compared with that of J2. Therefore, the slight difference  in the dynamical model has very limited impact on 
-the number of iterations and running time of the HL since the V init6 algorithm has high computational efficiency. 
-Therefore, the comparison among the three methods is still vali d.  
-The performance of the three methods is compared over 11 datase ts one per number of full revolutions from 0 
-to 10. Each dataset has 1000 samples, which are regenerated wit h the method described in Section III to validate the 
-DNN. The maximum iterations and tolerances of the three methods  are listed in Table 5.  
-Table 5 The maximum iterations an d tolerance of three methods 
-Algorithm Tolerance (km) Maximum iterations
-SN 0.001 2000 
-HL 0.001 10000 
-DNN-based method 0.001 2000 
-If the algorithm converges to a solution that meets the specifi ed tolerance within the set number of iterations, it is 
-recorded as a valid convergence, otherwise, as a failed converg ence. The result is displayed in Fig. 9 and Fig. 10, in 
-terms of convergence ratio (number of converged solutions over number of samples) and average number of 
-iterations to converge.  Fig.
-Fig. 10 A
-Acco r
-the valid 
-the num b
-the num b
-requires t
-revolutio n
-problem. mitigates . 9 The conv e
-Avera ge num
-rding to Fig. 9
-convergence r
-ber of iteratio n
-ber of iteratio n
-the least nu m
-ns is due the 
-For the sam e
-this problem ergence ratio 
-mber of iterat i
-9, the HL and t
-ratio of the S N
-ns of HL appe a
-ns of SN and t
-mber of iterati
-growing dif f
-e r e a s o n  t h e  H
-by providing a
-of different a
-ions of differ e
-the proposed m
-N decreases a
-ars to increas e
-the proposed D
-o n s .  T h e  l a c k
-ference betwe e
-HL progressi v
-a good initial 
-algorithms fo r
-ent al gorith m
-method coul d
-s the number 
-e linearly in l o
-DNN-based m
-k of converg e
-en the exact s
-vely requires 
-guess for eve r
-r the Jupiter J
-ms on the Jup i
-converge to t
-of revolution s
-og-scale as th e
-method remai n
-ence of the S N
-solution and t
-more iteratio n
-ry number of r
- 
-J2-perturbe d
- 
-iter J2-pertu r
-the required a c
-s increases. T h
-e number of r e
-n nearly const a
-N w i t h  t h e  i n
-the solution o
-ns to conver g
-revolutions.  
-d Lambert pr
-rbed Lambe r
-ccuracy in all 
-hen, accordin g
-evolutions inc r
-ant. The prop o
-ncrease in th e
-of the Kepler i
-ge .  T h e  p r o p o
- 
-27roblem 
-rt problem 
-cases, while 
-g to Fig. 10, 
-reases while 
-osed method 
-e number of 
-ian Lambert 
-osed method  The a
-only acc o
-DNN-Ba s
-CPU cal c
-of the re v
-the SCM
-the incre a
-time of t
-computatDNN eff
-e
-time wit h
-number o
-proposed shooting matrix. 
-T
-HL takes 
-Fig. 11 Aaverage CPU c
-ounts the ti m
-sed method a n
-culation time o
-volution incre a
-. In general, t
-ase in the nu m
-the SN and t h
-tional time of H
-ectively redu c
-h the number o
-of revolution s
-method are r
-algorith m, fo
-Their computa t
-less time, the 
-Avera ge CPU computationa l
-me of the S C
-nd HL are 0. 0
-of the propos e
-ases because t h
-the computati o
-mber of iterat i
-he proposed m
-HL appears t o
-ces the numb e
-of revolution s
-s tested in th
-respectively 0
-or which eac h
-tional time pe r
-HL requires m
-computatio nl time of the t
-CM .  F o r  z e r o
-051 seconds, 0
-ed method is t
-he accurate i n
-onal time inc r
-ions and the l
-method appe a
-o increase mo r
-er of iteration s
-s. The comput
-is pape r. The
-0.0082 s, 0.0 0
-h iteration ne e
-r iteration is h
-much more it e
- 
-nal time of di f
-three method s
-o-revolution c
-0.027 second s
-the shortest. T
-nitial guess ob t
-reases with th e
-longer propa g
-ar to increase 
-re rapidly. Th e
-s and provide s
-ational time o
-e average co m
-018 s, and 0. 0
-eds additional
-higher than th a
-erations than t h
-fferent metho
-s is given in F
-case, the av e
-s and 0.329 s e
-This advantag e
-tained using I
-e increase in t
-gation time. A
-linearly with 
-e figure show s
-s, as a result, 
-of the propose
-mputational t i
-0078 s. The p r
- t h r e e  i n t e g r a
-at of the HL. H
-he other two m
-ds for the Ju p
-Fig. 11, in w h
-erage CPU c o
-econds, respe c
-e becomes m o
-GG reduces t h
-the number o f
-As shown in F
-the number 
-s that the initi a
-a slower incr e
-d method is b
-ime per itera t
-ropose d m e t h
-al operations 
-However, tho u
-methods, as s h
- 
-piter J2-pert u
-hich the prop o
-omputation t i
-ctively. It is s
-ore obvious as 
-he number of 
-f revolutions, 
-Fig. 11, the c o
-of revolution s
-al guess obtai n
-ease of the c o
-below 0.5 sec o
-tion of SN, H
-ho d  a n d  S N  u
-to calculate t
-ugh the singl e
-hown in Fig. 1
-urbed Lamb e
-28osed method 
-ime of SN, 
-seen that the 
-the number 
-iterations of 
-due to both 
-omputational 
-s, while the 
-ned with the 
-omputational 
-onds, for the 
-HL ,  a n d  t h e  
-se the same 
-the Jacobian 
-e-iteration of 
-1. 
-ert problem C. Mon t
-In thi
-conditio n
-generate s
-time of s a
-methods, 
-transfer t i
-DNN is t
-called o n
-while the computatfinal res
-u
-increase i
-or larger sample g
-ete Carlo Ana l
-s section we 
-ns and transf e
-samples and t
-ample genera t
-four sets of M
-imes are per fo
-trained only o
-ne time per M
- solutions of t
-tions are perf o
-ults are given 
-in the numbe r 
-than 5000, t h
-eneration and 
-Fig. 12 Tota llysis 
-simulate the 
-er times and 
-train DNN be f
-tion, the traini n
-Monte Carlo s i
-formed. For e a
-once, using 2 0
-MC simulation 
-the J2-perturb
-ormed on the 
-in Fig. 12. I
-r of Lambert s
-he proposed m
-the training o f
-l CPU time o f
-repeated use 
-computing m
-fore using the 
-ng of the DN N
-imulations wi t
-ach set, the n u
-00000 sample s
-to generate t h
-ed Lambert p r
-personal co mp
-It can be see n
-olutions to b e
-method outper f
-f the DNN. 
-f different m e
-of the DNN-
-multiple J2-pe r
-proposed me t
-N and the SC M
-th 1000, 5000 ,
-umber revolu t
-s and the par a
-he first guess
-roblem using 
-mputer with In t
-n t h a t  t h e  e f fi
-e compute d. In
-forms the oth e
-ethods for th e
--based metho d
-rturbed Lam b
-thod, the total
-M. To compa r
-, 10000, and 1
-tions are equ a
-ameters settin g
-. The trainin g
-the proposed 
-tel Core-i7 4. 2
-ficiency of th e
-n particular, w
-er two metho d
-e Jupiter J2- p
-d b y  t a k i n g  a  
-bert solutions .
-l computation a
-re the total C P
-100000 sets o f
-ally distribute d
-g presented i n
-g of DNN wa
-method, HL a
-2 GHz CPU a
-e p r o p o s e d  m
-when the num b
-ds even when 
- 
-perturbed La
-random set o
-. Since it is 
-al time shoul d
-PU time of the 
-f boundary co n
-d between 0 a
-n previous se c
-s implemente
-and SN run in 
-and 128GB o f
-method i m p r o v
-ber of simulat i
-including th e
-mbert probl e
-29of boundary 
-essential to 
-d include the 
-above three 
-nditions and 
-and 10. The 
-ction, and is 
-d in Python 
-Matlab. All 
-f RAM. The 
-ves with the 
-ions is equal 
-e cost of the 
-em   In ad
-have bee n
-converge 
-Fig. 11. F
-0.024 se c
-due to its 
-Fig. 13 A
- 
-A fas t
-solve the 
-neural ne t
-the nove l
-J2-pertur b
-the Kepl e
-applied t oddition, two s t
-n teste d with t
-successfully a
-For the zero r e
-conds and 0.0 2
-longer time o
-Avera ge CP U
-t and novel m
-J2-perturbed 
-tworks, whic h
-l method is t o
-bed Lambert p
-erian solutio n
-o t h e  J u p i t e r  tress cases, w
-the proposed m
-and their ave r
-evolution cas e
-29 seconds, r e
-of flight for ea c
-U computatio n
-method using D
-Lambert pro b
-h has an excel l
-o u s e  a  D N N
-proble m. We 
-n and provide 
-J2-pertur bed 
-here the angl e
-method. For e a
-rage CPU co m
-e, the CPU co m
-espectively. T h
-ch revolution.
-nal time of t w
-VI.
-DNN and th e
-blem. DNN co
-lent performa n
-N to generate 
-demonstrated 
-good initial 
-Lambert pro b
-e between the 
-ach revolutio n
-mputational ti m
-mputation ti m
-he case of 36 0
- 
-wo stress cas e
-Conclus i
-e finite-differ e
-mposed of se v
-nce on appro x
-a first guess 
-that the DN N
-values for t h
-bl e m ,  t h e  e r r o
-initial and te r
-n, 100 MC tes t
-me is given in 
-me of the 180 d
-0 deg costs a 
-es for the Jup i
-ion  
-ence-based sh o
-veral layers is 
-ximating nonl i
-o f  t h e  c o r r e c
-N is capable o
-he subsequent 
-ors in the ini t
-rminal positio
-ts are perfor m
-Fig. 13, whic h
-degree and th e
-bit more tim e
- 
-iter J2-pertu r
-ooting algorit h
-the extensio n
-inear system. T
-ction of the i n
-f correcting t h
-differential c
-tial velocity a
-ns is 180 deg 
-med for each c a
-h is similar to 
-e 360 degree s
-e than the cas e
-rbed Lambe r
-hm has been 
-n of conventio n
-The major co n
-nitial velocit y
-he initial velo c
-correction me
-and terminal p
-30or 360 deg, 
-ase. All tests 
-the trend in 
-scenarios are 
-e of 180 deg 
-rt problem 
-proposed to 
-nal artificial 
-ntribution of 
-y t o  s o l v e  a  
-city error of 
-thod. When 
-position are 31 
- limited to 5m/s and 100 km, respectively. In addition, when com pared to a direct application of a shooting method 
-using Newton’s iterations and to a homotopy perturbed Lambert a lgorithm, the proposed method displayed a 
-computational time that appears to increase linearly with a slo pe of 0.047 with the number of revolutions. In the 
-application scenario presented in this paper the computational time is less than 0.5 seconds even for ten revolutions. 
-It was also shown that compared to a direct application of a sh ooting method it provides convergence to the required 
-accuracy in all the cases analyzed in this paper. Thus, we can conclude that the proposed DNN-based generation of a 
-first guess is a promising method to increase robustness and re duce computational cost of shooting methods for the 
-solution of the J2-pertubed Lambert problem. 
-The method proposed in this paper can be used to solve the J2-p erturbed Lambert problem for other celestial 
-bodies, by training the corresponding DNN with the correspondin g J2 a n d   parameters. Thus a library of 
-pre-trained DNN could be easily used to have a more general app lication to missions around any celestial body. On 
-the other hand, adding these dynamical parameters as part of th e training set would allow a single more general 
-DNN to be used with all celestial bodies. This latter option is  the object of the current investigation. 
-Acknowledgments 
-The work described in this paper was supported by the National Natural Science Foundation of China (Grant No. 
-11672126), sponsored by Qing Lan Project, Science and Technolog y on Space Intelligent Control Laboratory (Grant 
-No. 6142208200203 and HTKJ2020KL502019), the Funding for Outsta nding Doctoral Dissertation in NUAA 
-(Grant No. BCXJ19-12), State Scholarship from China Scholarship  Council (Grant No. 201906830066). The authors 
-fully appreciate their financial supports. 
- 32 
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-View publication statsView publication stats

txt/2202.08613.txt

@@ -1,396 +0,0 @@
-On the evaluation of (meta-)solver approaches
-Research Note
-On the evaluation of (meta-)solver approaches
-Roberto Amadini [email protected]
-Maurizio Gabbrielli [email protected]
-Department of Computer Science and Engineering,
-University of Bologna, Italy
-Tong Liu [email protected]
-Meituan,
-Beijing, China
-Jacopo Mauro [email protected]
-Department of Mathematics and Computer Science,
-University of Southern Denmark, Denmark
-Abstract
-Meta-solver approaches exploits a number of individual solvers to potentially build a
-better solver. To assess the performance of meta-solvers, one can simply adopt the metrics
-typically used for individual solvers (e.g., runtime or solution quality), or employ more
-specific evaluation metrics (e.g., by measuring how close the meta-solver gets to its virtual
-best performance). In this paper, based on some recently published works, we provide an
-overview of different performance metrics for evaluating (meta-)solvers, by underlying their
-strengths and weaknesses.
-1. Introduction
-A famous quote attributed to Aristotle says that “ the whole is greater than the sum of its
-parts”. This principle has been applied in several contexts, including the field of constraint
-solving and optimization. Combinatiorial problems arising from application domains such as
-scheduling, manufacturing, routing or logistics can be tackled by combining and leveraging
-the complementary strengths of different solvers to create a better global meta-solver .1
-Several approaches for combining solvers and hence creating effective meta-solvers have
-been developed. Over the last decades we witnessed the creation of new Algorithm Selec-
-tion (Kotthoff, 2016) and Configuration (Hoos, 2012) approaches2that reached peak results
-in various solving competitions (SAT competition, 2021; Stuckey, Becket, & Fischer, 2010;
-ICAPS, 2021). To compare different meta-solvers, new competitions were created, e.g., the
-2015 ICON challenge (Kotthoff, Hurley, & O’Sullivan, 2017) and 2017 OASC competition
-on algorithm selection (Lindauer, van Rijn, & Kotthoff, 2019). However, the discussion of
-why a particular evaluation metric has been chosen to rank the solvers is lacking.
-1. Meta-solvers are sometimes referred in the literature as portfolio solvers , because they take advantage of
-a “portfolio” of different solvers.
-2. A fine-tuned solver can be seen as a meta-solver where we consider different configurations of the same
-solver as different solvers.
-1arXiv:2202.08613v1  [cs.AI]  17 Feb 2022Amadini, Gabbrielli, Liu & Mauro
-We believe that further study on this issue is necessary because often meta-solvers are
-evaluated on heterogeneous scenarios, characterized by a different number of problems, dif-
-ferent timeouts, and different individual solvers from which the meta-solvers approaches
-are built. In this paper, starting from some surprising results presented by Liu, Amadini,
-Mauro, and Gabbrielli (2021) showing dramatic ranking changes with different, but rea-
-sonable, metrics we would like to draw more attention on the evaluation of meta-solvers
-approaches by shedding some light on the strengths and weaknesses of different metrics.
-2. Evaluation metrics
-Before talking about the evaluation metrics, we should spend some words on what we need
-to evaluate: the solvers. In our context, a solver is a program that takes as input the
-description of a computational problem in a given language, and returns an observable
-outcome providing zero or more solutions for the given problem. For example, for decision
-problems the outcome may be simply “yes” or “no” while for optimization problems we might
-be interested in the sub-optimal solutions found along the search. An evaluation metric , or
-performance metric, is a function mapping the outcome of a solver on a given instance to a
-number representing “how good” the solver on this instance is.
-An evaluation metric is often not just defined by the output of the (meta-)solver but
-can also be influenced by other actors such as the computational resources available, the
-problems on which we evaluate the solver, and the other solvers involved in the evaluation.
-For example, it is often unavoidable to set a timeouton the solver’s execution when there
-is no guarantee of termination in a reasonable amount of time (e.g., NP-hard problems).
-Timeouts makes the evaluation feasible, but inevitably couple the evaluation metric to the
-execution context. For this reason, the evaluation of a meta-solver should also take into
-account the scenario that encompasses the solvers to evaluate, the instances used for the
-validation, and the timeout. Formally, at least for the purposes of this paper, we can define
-a scenario as a triple (I;S;)where:Iis a set of problem instances, Sis a set of individual
-solvers,2(0;+1)is a timeout such that the outcome of solver s2Sover instance i2I
-is always measured in the time interval [0;).
-Evaluating meta-solvers over heterogeneous scenarios is complicated by the fact that
-the set of instances, solvers and the timeout can have high variability. As we shall see in
-Sect. 2.3, things are even trickier in scenarios including optimization problems.
-2.1 Absolute vs relative metrics
-A sharp distinction between evaluation metrics can be drawn depending on whether their
-value depend on the outcome of other solvers or not. We say that an evaluation metric is
-relativein the former case, absolute otherwise. For example, a well-known absolute metric
-is thepenalized average runtime with penalty 1(PAR) that compares the solvers by
-using the average solving runtime and penalizes the timeouts with times the timeout.
-Formally, let time (i;s; )be the function returning the runtime of solver son instance
-iwith timeout , assuming time (i;s; ) =ifscannot solve ibefore the timeout . For
-optimization problems, we consider the runtime as the time taken by sto solveito opti-
-2On the evaluation of (meta-)solver approaches
-mality3assuming w.l.o.g. that an optimization problem is always a minimization problem.
-We can define PAR as follows.
-Definition 1 (Penalized Average Runtime) .Let(I;S;)be a scenario, the PAR score of
-solvers2SoverIis given by1
-jIjP
-i2Ipar(i;s; )where:
-par(i;s; ) =(
-time (i;s; )iftime (i;s; )<
-
 otherwise.
-Well-known PAR measures are, e.g., the PAR 2adopted in the SAT competitions (SAT
-competition, 2021) or the PAR 10used by Lindauer et al. (2019). Evaluating (meta-)solvers
-with scenarios having different timeouts should imply a normalization of PARin a fixed
-range to avoid misleading comparisons.
-Another absolute metric for decision problems is the number (or percentage) of instances
-solved where ties are broken by favoring the solver minimizing the average running time,
-i.e., minimizing the PAR 1score. This metric has been used in various tracks of the planning
-competition (ICAPS, 2021), the XCSP competition (XCSP Competition, 2019), and the
-QBF evaluations (QBFEVAL, 2021).
-A well-established relative metric is instead the Borda count , adopted for example by the
-MiniZinc Challenge (Stuckey, Feydy, Schutt, Tack, & Fischer, 2014) for both single solvers
-and meta-solvers. The Borda count is a family of voting rules that can be applied to the
-evaluation of a solver by considering the comparison as an election where the solvers are
-the candidates and the problem instances are the voters. The MiniZinc challenge uses a
-variant of Borda4where each solver scores points proportionally to the number of solvers it
-beats. Assumingthat obj(i;s;t )isthebestobjectivevaluefoundbysolver sonoptimization
-problemiat timet, with obj(i;s;t ) =1when no solution is found at time t, the MiniZinc
-challenge score is defined as follows.
-Definition 2 (MiniZinc challenge score) .Let(S;I;)be a scenario where I=Idec[
-IoptwithIdecdecision problems and Ioptoptimization problems. The MiniZinc challenge
-(MZNC) score of s2SoverIisP
-i2I;s02Snfsgms(i;s0;)where:
-ms(i;s0;) =8
->>>>>>>><
->>>>>>>>:0 ifunknown (i;s)_better (i;s0;s)
-1 ifbetter (i;s;s0)
-0:5 iftime (i;s; ) =time (i;s0;)
-andobj(i;s; ) =obj(i;s0;)
-time (i;s0;)
-time (i;s; ) +time (i;s0;)otherwise
-where the predicate unknown (i;s)holds ifsdoes not produce a solution within the timeout:
-unknown (i;s) = (i2Idec^time (i;s; ) =)_(i2Iopt^obj(i;s; ) =1)
-3. Ifscannot solve ito optimality before , then time (i;s) =even if sub-optimal solutions are found.
-4. In the original definition, the lowest-ranked candidate gets 0 points, the next-lowest 1 point, and so on.
-3Amadini, Gabbrielli, Liu & Mauro
-andbetter (i;s;s0)holds ifsfinishes earlier than s0or it produces a better solution:
-better (i;s;s0) =time (i;s; )<time (i;s0;) =_obj(i;s; )<obj(i;s0;)
-This is clearly a relative metric because changing the set of available solvers can affect
-the MiniZinc scores.
-Tohandlethedisparatenatureofthescenarioswhencomparingmeta-solversapproaches,
-the evaluation function adopted in the ICON and OASC challenges was relative: the closed
-gap score . This metric assigns to a meta-solver a value in [0;1]proportional to how much it
-closes the gap between the best individual solver available, or single best solver (SBS), and
-thevirtual best solver (VBS),i.e.,anoracle-likemeta-solveralwaysselectingthebestindivid-
-ual solver. The closed gap is actually a “meta-metric”, defined in terms of another evaluation
-metric. Formally, if (I;S;)is a scenario and man evaluation metric to minimize, we have
-m(i;VBS;) = minfm(i;s; )js2Sgfor eachi2IandSBS = argmin
-s2SP
-i2Im(i;s; ).
-We can define the closed gap as follows.
-Definition 3 (Closed gap) .Letmbe an evaluation metric to minimize for a scenario
-(I;S;), and letmVBS=P
-i2Im(i;VBS;)andmSBS=P
-i2Im(i;SBS;). Theclosed
-gapof a (meta-)solver sw.r.t.mon that scenario is:
-mSBSP
-i2Im(i;s; )
-mSBSmVBS
-If not specified, we will assume the closed gap computed w.r.t. the PAR 10score as done
-in the AS challenges 2015 and 2017.5
-2.2 A surprising outcome
-An interesting outcome reported in Liu et al. (2021) was the profound di��erence between
-the closed gap and the MiniZinc challenge scores. Liu et al. compared the performance of
-six meta-solvers approaches across 15 decision-problems scenarios taken from ASlib (Bischl
-et al., 2016) and coming from heterogeneous domains such as Answer-Set Programming,
-Constraint Programming, Quantified Boolean Formula, Boolean Satisfiability.
-Tab. 1 reports the performance of ASAP and RF, respectively the best approach accord-
-ing to the closed gap score and the MZNC score. The scores in the four leftmost columns
-clearly show a remarkable difference rank if we swap the evaluation metric. With the closed
-gap, ASAP is the best approach and RF the worst among all the meta-solvers considered,
-while with the MZNC score RF climbs to the first position while ASAP drops to the last
-position.
-Another thing that catches the eye in Tab. 1 is the presence of negative scores . This
-happens because, by definition, the closed gap has upper bound 1 (no meta-solver can
-improve the VBS) but not a fixed lower bound. Hence, when the performance of the meta-
-solver is worse than the performance of the single best solver, the closed gap drops below
-zero. While on a first glance this seems reasonable—meta-solvers should perform no worse
-than the individual solvers—it is worth noting that the penalty for performing worse than
-5. In the 2015 edition, the closed gap was computed as 1mSBSm(i;s; )
-mSBSmVBS=mVBSm(i;s; )
-mVBSmSBS.
-4On the evaluation of (meta-)solver approaches
-Table 1: Comparison ASAP vs RF. The MZNC column reports the average MZNC score
-per scenario. Negative scores are in bold font.
-Closed gap MZNC Better than other
-Scenario ASAP RF ASAP RF ASAP RF
-ASP-POTASSCO 0.7444 0.5314 2.2235 2.6163 275 671
-BNSL-2016 0.8463 0.7451 1.2830 3.0250 98 993
-CPMP-2015 0.6323 0.1732 2.0501 2.3660 137 334
-CSP-MiniZinc-Time-2016 0.6251 0.2723 2.1552 2.7214 17 53
-GLUHACK-2018 0.4663 0.4057 1.9040 2.4528 62 147
-GRAPHS-2015 0.758-0.6412 2.3045 3.3731 489 3663
-MAXSAT-PMS-2016 0.5734 0.3263 1.4747 2.8616 66 439
-MAXSAT-WPMS-2016 0.7736-1.1826 1.5168 2.4043 126 386
-MAXSAT19-UCMS 0.6583-0.2413 2.0893 2.5189 145 269
-MIP-2016 0.35-0.3626 2.4035 2.4239 81 105
-QBF-2016 0.7568-0.1366 1.8642 2.7154 193 467
-SAT03-16_INDU 0.3997 0.1503 2.1508 2.5812 491 1116
-SAT12-ALL 0.7617 0.6528 1.6785 2.8250 262 1227
-SAT18-EXP 0.5576 0.3202 1.9239 2.4998 61 164
-TSP-LION2015 0.4042-19.1569 2.4352 2.6979 1115 1949
-Tot. 9.3077-18.1439 29.4573 40.0826 3618 11983
-Tot.TSP-LION2015 8.9035 1.013 27.0221 37.3846 2503 10034
-the SBS also depends on the denominator mSBSmVBS. This means that in scenarios
-where the performance of the SBSis close to the perfect performance of the VBSthis
-penalty can be significantly magnified. The TSP-LION2015 scenario is a clear example: the
-RF approach gets a penalization of more than 19 points, meaning that RF should perform
-flawlessly in about 20 other scenarios to expiate this punishment. In fact, in TSP-LION2015
-thePAR 10distributions of SBSandVBSare very close: the SBSis able to solve 99.65% of
-the instances solved by the VBS, leaving little room for improvement. RF scores -19.1569
-while still solving more than 90% of the instances of the scenario and having a difference
-with ASAP of slightly more than 5% instances solved.
-Why are the closed gap and the MZNC rankings so different? Looking at the rightmost
-twocolumnsinTab.1showing, foreachscenario, thenumberofinstanceswhereanapproach
-is faster than the other, one may conclude that RF is far better than ASAP. In all the
-scenarios the number of instances where its runtime is lower than ASAP runtime is greater
-than the number of instances where ASAP is faster. Overall, it is quite impressive to see
-that RF beats ASAP on 11983 instances while ASAP beats RF on 3618 times only.
-An initial clue of why this happens is revealed in Liu et al. (2021), where a parametric
-versionofMZNCscoreisused. Inpractice,Def.2isgeneralizedbyassumingtheperformance
-of two solvers equivalent if their runtime difference is below a given time threshold .6This
-variantwasconsideredbecauseatimedifferenceoffewsecondscouldbeconsideredirrelevant
-6. Formally, ifjtime (i;s; )time (i;s0;)jthen bothsands0scores 0.5 points—note that if = 0we
-get the original MZNC score as in Def. 2.
-5Amadini, Gabbrielli, Liu & Mauro
-Figure 1: Cumulative Borda count by varying the threshold.
-Figure 2: Solve instances difference ASAP vs RF.
-if solving a problem can take minutes or hours. The parametric MZNC score is depicted in
-Fig. 1, where different thresholds are considered on the x-axis. It is easy to see how the
-performance of ASAP and RF reverses when increases: ASAP bubbles from the bottom
-to the top, while RF gradually sinks to the bottom.
-Let us further investigate this anomaly. Fig. 2 shows the runtime distributions of the
-instances solved by ASAP and RF, sorted by ascending runtime. We can see that ASAP
-solves more instances, but for around 15k instances RF is never slower than ASAP. Sum-
-6On the evaluation of (meta-)solver approaches
-Table 2: Average closed gap, speedup, and normalized runtime. Peak performance in bold
-font.
-Meta-solver Closed gap Speedup Norm. runtime
-ASAP 0.4866 0.4026 0.8829
-sunny-as2 0.4717 0.4122 0.8879
-autofolio 0.4713 0.4110 0.8855
-SUNNY-original 0.4412 0.3905 0.8790
-*Zilla 0.3416 0.3742 0.8753
-Random Forest -0.1921 0.3038 0.8507
-marizing, ASAP solves more instances but RF is in general quicker when it solves an (often
-easy) instance. This entails the significant difference between closed gap and Borda metrics.
-Inouropinion, ontheonehand, itisfairtothinkthatASAPperformsbetterthanRFon
-these scenarios. The MZNC score seems to over-penalize ASAP w.r.t. RF. Moreover, from
-Fig. 1 we can also note that for 103the parametric MZNC score of RF is still better,
-but 103 seconds looks quite a high threshold to consider two performances as equivalent.
-On the other hand, the closed gap score can also be over-penalizing due to negative outliers.
-We also would like to point out that the definitions of SBSfound in the literature do
-not clarify how it is computed in scenarios where the set of instances Iis split into test and
-training sets. Should the SBSbe computed on the instances of the training set, the test set,
-or the whole dataset I? One would be inclined to use the test set to select the SBS, but this
-choice might be problematic because the test set is usually quite small w.r.t. the training set
-when using, e.g., cross-validation methods. In this case issues with negative outliers might
-be amplified. If not clarified, this could lead to confusion. For example, in the 2015 ICON
-challenge the SBSwas computed by considering the entire dataset (training and testing
-instances together). In the 2017 OASC instead the SBSwas originally computed on the
-test set of the scenarios, but then the results were amended by computing the SBSon the
-training set.
-An alternative to the above metrics is the speedupof a single solver w.r.t. the SBSor the
-VBS, i.e., how much a meta-solver can improve a baseline solver. Tab. 2 reports, using the
-data of (Liu et al., 2021), for each meta-solver sin a scenario (I;S;)the average speedup
-computed as1
-jIjP
-i2Itime (i;VBS;)
-time (i;s; ). Unlike the closed gap, that has no lower bound, the
-speedup always falls in [0;1]with bigger values meaning better performance. We compared
-this with the average normalized runtime score, computed as 11
-jIjP
-i2Itime (i;s; )
-and the
-average closed gap score w.r.t PAR 1. We use PAR 1instead of PAR 10to be consistent with
-speedup and normalized runtime, which do not get any penalization.
-The rank with speedup and normalised runtime is the same. The podium changes if we
-use the closed gap: in this case sunny-as2 and autofolio lose one position while ASAP rises
-from third to first position. However, as we shall see in the next section, the generalization
-of these metrics to optimization problems is not trivial.
-7Amadini, Gabbrielli, Liu & Mauro
-2.3 Optimization problems
-So far we have mainly talked about evaluating meta-solvers on decision problems. While the
-MZNC score takes into account also optimization problems, for the closed gap, (normalized)
-runtime, and speedup the generalization is not as obvious as it might seem. Here using
-the runtime might not be the right choice: often a solver cannot prove the optimality of a
-solution, even when it actually finds it. Hence, the obvious alternative is to consider just
-the objective value of a solution. But this value needs to be normalized , and to do so what
-bounds should we choose? Furthermore: how to reward a solver that actually proves the
-optimality of a solution? And how to penalize solvers that cannot find any solution?
-Ifsolvingtooptimalityisnotrewarded, metricssuchastheratioscoreofthesatisfiability
-track of the planning competition can be used.7This score is computed as the ratio between
-the best known solution and the best objective value found by the solver, giving 0 points in
-case no solution is found.
-A different metric that focuses on quickly reaching good solution is the areascore,
-introduced in the MZNC starting from 2017. This metric computes the integral of a step
-function of the solution value over the runtime horizon. Intuitively, a solver that finds good
-solutions earlier can outperform a solver that finds better solutions much later in the solving
-stage.
-Other attempts have been proposed to take into account the objective value and the run-
-ning times. For example, the ASP competition (Calimeri, Gebser, Maratea, & Ricca, 2016)
-adopted an elaborate scoring system that combines together the percentage of instances
-solved within the timeout, the evaluation time, and the quality of a solution. Similarly,
-Amadini, Gabbrielli, and Mauro (2016) proposed a relative metric where each solver sgets
-a reward inf0g[[;][f1gaccording to the objective value obj(s;i; )of the best solution
-it finds, with 01. If no solution is found then sscores 0, if it solves ito optimality
-it scores 1, otherwise the score is computed by linearly scaling obj(s;i; )in[;]according
-to the best and worst objective values find by any other available solver on problem i.
-2.4 Randomness and aggregation
-We conclude the section with some remarks about randomness and data aggregation.
-When evaluating a meta-solver son scenario (I;S;), it is common practice to partition
-Iinto a training set Itr, on which s“learns” how to leverage its individual solvers, and a
-test setItswhere the performance of son unforeseen problems is measured. In particular,
-to prevent overfitting, it is possible to use a k-fold cross validation by first splitting Iinto
-kdisjoint folds, and then using in turn one fold as test set and the union of the other
-folds as training set. In the AS challenge 2015 (Lindauer et al., 2019) the submissions were
-indeed evaluated with a 10-fold cross validation, while in the OASC in 2017 the dataset of
-the scenarios was divided only in one test set and one training set. As also underlined by
-the organizers of the competition, this is risky because it may reward a lucky meta-solver
-performing well on that split but poorly on other splits.
-Note that so far we have always assumed deterministic solvers, i.e., solvers always provid-
-ing the same outcome if executed on the same instance in the same execution environment.
-Unfortunately, the scenario may contain randomized solvers potentially producing different
-7. This track includes optimization problems where the goal is to minimize the length of a plan.
-8On the evaluation of (meta-)solver approaches
-results with a high variability. In this case, solvers should be evaluated over a number of
-runs and particular care must be taken because the assumption that a solver can never
-outperform the VBS would be no longer true.
-A cautious choice to decrease the variance of model predictions would be to repeat the
-k-fold cross validation n>1times with different random splits. However, this might imply
-a tremendous computational effort—the training phase of a meta-solver might take hours or
-days—and therefore a significant energy consumption. This issue is becoming an increasing
-concern. For example, in their recent work Matricon, Anastacio, Fijalkow, Simon, and Hoos
-(2021) propose an approach to early stop running an individual solver that it is likely to
-perform worse than another solver on a subset of the instances of the scenario. In this way,
-less resources are wasted for solvers that most likely will not bring any improvement.
-Finally, we spend a few words on the aggregation of the results. It is quite common
-to use the arithmetic mean, or just the sum, when it comes to aggregate the outcomes
-of a meta-solver over different problems of the same scenario (e.g., when evaluating the
-results on the n
ktest sets of a k-fold cross validation repeated ntimes). The same
-applies when evaluating different scenarios. The choice of how to aggregate the metric
-values into a unique value should however be motivated since the arithmetic mean can lead
-to misleading conclusions when summarizing normalized benchmark (Fleming & Wallace,
-1986). For example, to amortize the effect of outliers, one may use the median or use the
-geometric mean to average over normalized numbers.
-3. Conclusions
-As it happens in many other fields, the choice of reasonable metrics can have divergent
-effectsontheassessmentof(meta-)solvers. Whiletheseissuesaremitigatedwhencomparing
-individual solvers in competitions having uniform scenarios in term of size, difficulty, and
-nature, the comparison of meta-solver approaches poses new challenges due to diversity of
-the scenarios on which they are evaluated. Although it is impossible to define a fits-all
-metric, we believe that we should aim at more robust metrics avoiding as much as possible
-the under- and over-penalization of meta-solvers.
-Particular care should be taken when using relativemeasurements, because the risk is
-to amplify small performance variations into large differences of the metric’s value. Present-
-ing the results in terms of orthogonal evaluation metrics allows a better understanding of
-the (meta-)solvers performance, and these insights may help the researchers to build meta-
-solvers that better fit their needs, as well as to prefer an evaluation metric over another.
-Moreover, well-established metrics may be combined into hybrid “meta-metrics” folding to-
-gether different performance aspects and handling the possible presence of randomness.
-9Amadini, Gabbrielli, Liu & Mauro
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@@ -1,786 +0,0 @@
-CELLNUCLEI CLASSIFICATION INHISTOPATHOLOGICAL
-IMAGES USING HYBRID OLCONV NET
-Suvidha Tripathi
-Department of Information Technology
-Indian Institute of Information Technology Allahabad
-Jhalwa, Deoghat, Prayagraj, Uttar Pradesh 211015
-[email protected] Kumar Singh
-Department of Information Technology
-Indian Institute of Information Technology Allahabad
-Jhalwa, Deoghat, Prayagraj, Uttar Pradesh 211015
-[email protected]
-February 22, 2022
-ABSTRACT
-Computer-aided histopathological image analysis for cancer detection is a major research challenge
-in the medical domain. Automatic detection and classification of nuclei for cancer diagnosis impose
-a lot of challenges in developing state of the art algorithms due to the heterogeneity of cell nuclei
-and data set variability. Recently, a multitude of classification algorithms has used complex deep
-learning models for their dataset. However, most of these methods are rigid and their architectural
-arrangement suffers from inflexibility and non-interpretability. In this research article, we have pro-
-posed a hybrid and flexible deep learning architecture O LConvNet that integrates the interpretability
-of traditional object-level features and generalization of deep learning features by using a shallower
-Convolutional Neural Network (CNN) named as CNN 3L.CNN 3Lreduces the training time by
-training fewer parameters and hence eliminating space constraints imposed by deeper algorithms.
-We used F1-score and multiclass Area Under the Curve (AUC) performance parameters to compare
-the results. To further strengthen the viability of our architectural approach, we tested our proposed
-methodology with state of the art deep learning architectures AlexNet, VGG16, VGG19, ResNet50,
-InceptionV3, and DenseNet121 as backbone networks. After a comprehensive analysis of classi-
-fication results from all four architectures, we observed that our proposed model works well and
-perform better than contemporary complex algorithms.
-Keywords Deep Learning, Hybrid networks, Object level features, Transfer Learning, Histopathological Images,
-Cell Nuclei Classification, Class balancing, Convolutional Neural Networks, Multi Layer Perceptron
-1 Introduction
-Early cancer detection is a major challenge in the medical domain. Even today the medical community is largely
-dependent upon the expert pathologist for detecting and classifying such cell anomalies that cause cancer, in whole
-slide histopathological images. The job of the pathologists becomes very cumbersome and may take several days for
-annotating the whole slide images of biopsy samples. Moreover, the reliability of predictions also depends upon the
-experience of the pathologist and some times, consensus of more than one pathologists are required for confirming
-such anomalies. These factors provide adequate motivation for research and development of a computer-assisted
-diagnostic (CAD) systems which classifies cell nuclei and improves the understanding of some of the underlying
-biological phenomenon, e.g., monitoring cancer cell cycle progress [1], type, shape, size and arrangement of the cells
-in the affected organ sites, and the knowledge about metastasis, if the cells are present at some unlikely locations .
-All these observations can be comprehended if we know the type of cell present in the diseased tissue sample. Early
-diagnosis of cell anomalies can largely affect the disease prognosis [2]. Such as in the case of a colon or colorectal
-carcinoma, epithelial cells lining the colon or rectum of the gastrointestinal tract are affected and timely detection of
-these cells can help in quick diagnosis, which eventually would increase the prognostic value of the disease. Similarly,
-the lymphocytes can also be analyzed for sentinel lymph node disease [2]. The other examples are the Myeloma orarXiv:2202.10177v1  [cs.CV]  21 Feb 2022APREPRINT - FEBRUARY 22, 2022
-Figure 1: Example sub-images of different classes of nuclei starting from first row to fourth: Epithelial, Fibroblasts,
-Inflammatory, Miscellaneous (in sets of 2 - Adipocyte, Endothelial, Mitotic Figure, and Necrotic Nucleus (from left
-to right)
-multiple Myeloma detections through the plasma cells which are the types of the white blood cells and cause the
-cancer [3]. Therefore, a sample biopsy from a specific location can be quickly analyzed using the information of cell
-environment provided by appropriate CAD system.
-In particular medical image analysis, for all diagnosis, is attributed to the knowledge and skills possessed by trained and
-experienced pathologists. Although pathologists have the ability and means to single out the affected cancerous lesions
-in a tissue biopsy samples, most of such detections are still done manually and hence time-consuming. Numerous
-challenges are involved in diagnosing cancer due to data set variability and heterogeneity in cell structures, which
-makes the process extremely tedious even for experts. Software intervention for early detection is therefore important
-for the purpose of effective control and treatment of the diseased organs [4]. To develop such automated cell detection
-and classification algorithms, the knowledge of histology is vital and requires the annotated or labeled data set to
-be prepared by the expert histo-pathologists. Once the labelled data is acquired, then the routine intervention of
-pathologists can be eliminated while analyzing the whole slide samples under test by using the developed automated
-CAD algorithms. Cell nuclei in a Hematoxylin and Eosin (H&E) stained histopathological slide sample have a specific
-shade of blue caused by hematoxylin’s reaction with cellular protein present in the nuclei of the cells [5]. A shape
-of the cell varies with cell type, cell-cycle stage, and also with the presence or absence of cancer. Fig. 1 shows four
-different classes of nuclei namely, Inflammatory, Fibroblast, Epithelial, and Miscellaneous, which include adipocyte,
-endothelial nucleus, mitotic figures, and necrotic nucleus [6]. The nuclei structures as shown in Fig.1 have different
-shapes, texture, and intensity features, which vary by the factors i.e. nuclei type (epithelial, fibroblasts, lymphocytes,
-etc.), the malignancy of the disease (or grade of cancer), and nuclei life cycle (interphase or mitotic phase) [7]. For
-example, the inflammatory nuclei, a type of white blood cells and called as lymphocyte nuclei (LN) are smaller in size
-and regular spherical shape as compared to epithelial nuclei (EN) [8]. Fibroblasts have long spindle-like shape and
-appearance and having very little cytoplasm [9]. Activated fibroblasts and infiltrating inflammatory cells are the signs
-of potential tumor growth [9]. All these histological and biological differences between cell structures and their site
-of origin highlight the clinical relevance of classifying different types of nuclei.
-In this paper, we have undertaken the feature based approach for automated nuclei classification. Feature based
-approach can be classified into two general categories: hand-crafted features and deep learning based features. In
-histopathology Images, morphological and architectural features whose accuracy depends on the amount of magnifi-
-cation and the type of class and which has unique mixture of visual pattern qualify as hand crafted features whereas
-unsupervised deep learning features are intuitive and are a by-product of filter responses obtained from large number
-of training samples and fine tuning of the network. In the proposed work we clearly proved the benefits of using
-combined feature set consisting both object level features and learned deep learning features over feature set acquired
-from single domain on complex medical data. Moreover, for detailed analysis, the accuracy-generalization tradeoff
-and space-time complexity issues as exhibited by traditional and DL methods respectively have been considered in the
-proposed architectural arrangement.
-In summary, key contributions of this work include:
-1. The strength of our method lies in the flexible architecture that supports a backbone of deep learning model
-to extract deep features and a simple object level extraction framework for extracting cell level features.
-2APREPRINT - FEBRUARY 22, 2022
-2. We achieved a high level of nuclei classification system through simple concatenation of derived features
-from two domains.
-3. The emphasis has been put through a series of experiments that in case of nuclei structures, even very small
-number of basic and locally focussed object level features can enhance the performance if combined with
-three or more layers of deep learning architecture.
-4. To the best of our knowledge, this is the first study on this hypothesis that have developed a custom architec-
-ture for the problem to highlight the need of designing lighter architectures for specific problems rather than
-using deeper pre-trained architectures.
-5. To the best of our knowledge, this is the first study on this hypothesis to have experimentally prove the
-performance of end to end learning over stage wise learning.
-The rest of the paper is organized as follows. Section-II describes the reviewed literature for handcrafted and deep
-features. Section-III describes the complete methodology of the proposed work. The experimental setup is elaborated
-in Section-IV including the database and workflow. Section-V contains various results and necessary discussion.
-Discussion section also justifies the appropriateness of the proposed method while the flexibility and robustness are
-the points of concerns. Section-VI concludes the work presented in the paper followed by the acknowledgments and
-references.
-2 Reviewed Literature
-Owing to the above-mentioned properties exhibited by cell nuclei, many traditional handcrafted cell nuclei classi-
-fication algorithms have been reported in [10–16]. Authors in [10] have first segmented the nuclei objects using
-morphological region growing, wavelet decomposition and then found the shape and texture features for classifying
-cancer cells vs. normal cells using SVM classifier. Another handcrafted feature-based method for cell nuclei clas-
-sification in histopathological images while using shape, statistical, and texture (Gabor and Markov Random Field)
-features from localized cells has been reported in [11]. Other object level (OL) feature extraction from localized cell
-objects based methods have been reported in [12]. All the methods [10–12, 15, 16] using the OL features have been
-critically analyzed against the utility of those features for individual problems related to a histological and/or cytologi-
-cal analysis of cancer cells in [13,14]. The quality of the extracted features from various handcrafted methods [10–16]
-is then assessed after passing them through appropriate classifiers. The success of their findings motivated the use
-of targeted OL features in our methodology. To design effective handcrafted feature-based models requires complex
-algorithms to achieve high performance and a decent level of domain-specific knowledge [15, 16]. Moreover, it be-
-comes extremely difficult to resolve the issues due to dataset variability and heterogeneity within each cell type. These
-issues lead to the inability of the reported novel but complex models to generalize well with varying datasets. It is
-worth mentioning that, most of these methods are reported on a very small sample size in general, causing robustness
-issues. To overcome the generalization problem, it is required to model features, which are common for a particular
-class of cell nuclei but highly discriminating among different classes. Recently, deep learning architectures have been
-known to produce generalized feature sets and hence have proved their niche in classification algorithms [17–24].
-To put it more clearly, the key advantage of using deep learning architectures can be explained by highlighting the
-problems of linear or other shallow classifiers. The traditional classifiers do not use raw pixel data and possibly cannot
-distinguish two similar objects on different backgrounds which is a case of selectivity-invariance dilemma [17]. That
-is why we need good feature extractors with such classifiers to solve the selectivity-invariance dilemma. The Deep
-Learning (DL) based architectures automatically learn the good feature sets from the large histopathological image
-data sets. In 2016, the CAMELYON challenge has also reported the use of extensive deep learning architectures for
-solving various problems of Localization and Classification. Detailed methodologies of all these methods have been
-reported in [21]. More recently, authors in [25] have used pre-trained VGG19 to classify extensively augmented multi
-grade brain tumour samples whereas authors in [26] to identify alcoholism in subjects using their brain scans. So,
-DL methods find applicability in wide range of applications due to their robust and better performing architectures.
-However, there are some issues with deep learning based methods as well. DL features lack interpretability and can-
-not be confirmed as global or local features. Moreover, there is always the lack of a large number of datasets in the
-medical domain, which hampers or restricts DL algorithms to scale well on all other test data sets not used for the
-training. Another major issue with deep architectures is the huge parameters with greater depths, causing the opti-
-mization problem very time-consuming. At the same time, the complexity of a model increases, as the depth increases
-and eventually the intermediate processes become less and less interpretable. One of the approaches for minimizing
-the training time on the medical dataset is to use the concept of transfer learning and fine-tuning pre-trained models
-such as AlexNet [18], VGG16, VGG19 [20], ResNet50 [19], DenseNet [27], and InceptionV3 [28]. Originally these
-models have been trained on natural image datasets, which is from an entirely different domain but can be fine-tuned to
-extract features on medical images. But, medical data has very little to no correspondence with natural images. Hence,
-3APREPRINT - FEBRUARY 22, 2022
-relying solely on transfer learning based fine-tune approach should not be preferred. Rather, the training should be
-done on the networks that have either not been pre-trained on natural images or have been pre-trained on similar medi-
-cal dataset. But, training DL networks on medical dataset has its own set of challenges, including lack of huge amount
-of annotated medical data for training. Moreover, the diverse nature of medical images prevents the generalization and
-standardization of datasets on which DL networks could be trained for transfer learning.
-An exhaustive survey of deep learning methods as reported in [29] thoroughly highlights the merits of applying DL
-methods in the field of medical imaging, medical informatics, translational bioinformatics, and public health. The
-amalgamated use of both OL and DL features for the purpose of nuclei detection, segmentation, and classification
-have also been suggested in [8, 29].
-Therefore, OL features in combination with DL features could help to bridge the gap between issues that two domains
-bring individually. Some recent articles have worked on the similar hypothesis of inter-domain feature combination
-and developed a method that combines the two feature sets as reported in [22,23]. But, the drawback of their method is
-in their complexity and huge training times due to very deep network models. Authors in [22] combined different deep
-learning features extracted from Caffe-ref [30], VGG-f [31] and VGG19 models with Bag of Features (BoF) and Local
-Binary Pattern (LBP) features. They then used ensemble classifiers to produce better classification accuracy than that
-of the softmax classification method used by deep learning models. However, the dataset under experiments in [22]
-was imbalanced hence, the reported accuracy trend may not hold good for other imbalanced datasets which are highly
-probable in case of medical image datasets. F1 score and AUC are better parameters for assessing the performance of
-classification algorithms for imbalanced datasets. Also, the authors [22, 23] reported the complex models which were
-based on pre-trained deep architectures with 7 and more layers and did not analyze the performance trend on other
-customized architectures that could have minimized the space and time constraints. It is difficult to design and test
-such relatively inflexible algorithms on a new dataset and deploy in real time applications. For example, it is difficult
-to change the design if one wishes to add a new functionality and re-train the algorithm. Furthermore, the reported
-handcrafted features in these studies lack direct relevance to the nuclei structural properties.
-3 METHODOLOGY
-Hybrid feature based flexible classification framework trained on a dataset from [32] is used to determine the suit-
-ability of combining different feature sets. Few pre-processing steps are performed to segment the cell nuclei from
-background stroma. This step is necessary to extract the OL features. This feature set comprises relevant visual, shape
-and texture features from each nucleus. DL feature is extracted from the original input images. Both the set of features
-are then fused to produce a final feature set. The final fused feature set has been used by Multi-Layer Perceptron
-(MLP) as input for classifying the cell nuclei into one of the four categories. The block diagram of the proposed
-architectural setup has been shown in Fig. 2.
-The entire flow has been modeled in the Algorithm-1.
-Various steps involved in the proposed methodology has been elaborated in the following sub-sections (3.1)-(3.5)
-3.1 Segmentation
-Cytologic and histologic images prevent the generalization of segmentation algorithms because of the inherent vari-
-ability of the nuclei structures present in them. Due to this reason, determining which state of the art algorithm for
-nuclei segmentation would work for our dataset was a lengthy problem. Therefore, we seek to develop application-
-specific segmentation algorithm for OL feature extraction. Our dataset contains H&E (Hematoxylin and Eosin) stained
-RGB image blocks that stains the nuclei region in bright blue and cell region in pink. The staining helped us to roughly
-extract the nucleus contour. Segmentation of an object then allowed for the calculation of OL features such as homo-
-geneous color, texture, size, and shape of the segmented region.
-Firstly, we enhanced the blue intensity of the nuclei through contrast adjustment. For this purpose blue color channel
-intensities were mapped from initial values to 255. Similarly, Red and Green channel pixel values less than a certain
-range were also tweaked towards higher range. This technique of adjusting intensity values in each channel to new
-values in an output image helped in highlighting poorly contrasted nuclei regions from cell cytoplasm and background
-noise. We assigned a higher value to blue intensity pixels relative to red and green components because blue-ratio is
-proven to be capable of highlighting nuclei regions in H&E stained histopathological images [33]. This step is followed
-by color normalization so that the intensity values follow normal distribution and as well remove any noise/artefact
-that may have been introduced due to contrast enhancement. For the next step, we computed the binary image and
-calculated the convex hull of the labelled region having the highest number of pixels. Convex hull of the binary
-image ensured that the largest area containing most blue pixels is retained and the defined boundary of the nuclei can
-4APREPRINT - FEBRUARY 22, 2022
-Figure 2: Block Diagram of proposed OLConvNet. Raw training images of cell nuclei is passed through Branch-1 of
-the network for DL feature extraction and further for classification using fully connected (FC1) and softmax layer of
-the DL network (OUTPUT-1). OL features are extracted from segmented nuclei images after segmentation pipeline.
-Extracted OL features are classified in Branch 2. Switch between branch 1 and 2 make the decision about which kind
-of output we would want for our dataset (OUTPUT-1 or OUTPUT-2 or Both).
-be obtained for calculating OL features. In other words, the perturbation in nuclei structure due to staining process
-may distort original nuclei structures, so obtaining a convex hull defines the smooth boundary around the nucleus.
-This further helps in following procedural steps of extracting OL features. Convex hull step is then followed by edge
-extraction of the convex hull. Lastly, we did the scalar multiplication of the resultant image with the original image to
-obtain the final output of a segmented RGB Nuclear image. Segmentation results helped in delineating nuclei region
-from the surrounding tissues. Figure 3 shows the pipeline of segmentation. Some of the segmented classwise nuclei
-examples are shown in Figure 4.
-Figure 3: Segmentation Pipeline of our network
-5APREPRINT - FEBRUARY 22, 2022
-Algorithm 1 OLConvNet
-Input: Training data set Dtrwith msamples. Data fi(X), where i= 1;_:::; m is an instance in the 3 dimensional
-image space X2<UVdandyi2Y=f1;2;3;4gis the class identity label associated with fi(X).
-Output: Output1: ^ycnn1-of-4 Softmax output from CNN 3L, Output2: ^ymlp1-of-4 Softmax output from MLP
-Pre - Processing (Balancing Dataset) :ffi(X); yigM ADASY N (ffi(X); yigm).M - samples after balancing
-dataset, ADASYN - a function used for balancing the dataset
-forxi2Xdo
-SEGMENTATION :xs
-i SegN (xi) .SegN: a function following segmentation pipeline
-OBJECT LEVEL FEATURES :(FVi
-OL)MN1 OLFV (xs
-i)
-.OLFV: a function extracting nuclei features, N1- Number of Object Level features
-STAGEWISE - TRAINING :
-forepoche2(1;100) do
-forbatch b do
-forxi2bdo
-CNN Inference: ^yicnn CNN !cnn(xi)
-@L
-@!cnn ^ycnn
-i;yi .!- weight parameter
-Compute@L
-@!cnnusing backpropagation .L - cross-entropy Loss function
-Update CNN: !cnn !cnn@L
-@!cnn .- Learning rate
-ExtractFeatures:fFVcnng(MN2) CNN 3L(ff(X)g;layer ) .N 2- Number of CNN features
-Concatenation:fTotalFeaturesgM(N1+N2) FVcnn+FVOL
-forTFi2TotalFeatures do
-MLP Inference: ^yimlp MLP!mlp(TFi)
-@L
-@!mlp ^ymlp
-i;yi
-Compute@L
-@!mlpusing backpropagation
-Update MLP: !mlp !mlp@L
-@!mlp
-END to END - TRAINING :
-forepoche2(1;100) do
-forbatch b do
-forxi2bdo
-CNN Inference: ^yicnn CNN !cnn(xi)
-@L
-@!cnn ^ycnn
-i;yi
-Compute@L
-@!cnnusing backpropagation
-Update CNN: !cnn !cnn@L
-@!cnn
-ExtractFeat:fFVi
-cnng(1N2) CNN 3L(ffi(X)g;layer )
-Concatenation:fTotalFeaturesig1(N1+N2) FVi
-cnn+FVi
-OL
-MLP Inference: ^yimlp MLP!mlp(TotalFeaturesi)
-@L
-@!mlp ^ymlp
-i;yi
-Compute@L
-@!mlpusing backpropagation
-Update MLP: !mlp !mlp@L
-@!mlp
-Figure 4: Example segmented-images of different classes of nuclei starting from first row to fourth: Epithelial nuclei,
-Fibroblasts, Inflammatory nuclei, and Miscellaneous
-6APREPRINT - FEBRUARY 22, 2022
-3.2 Object Level Feature Extraction
-We have extracted a set of nine features. These nine features include color, texture and shape information of the nuclei
-calculated on the segmented nuclei image. Color information contains the pixel intensity value that has the highest
-frequency in the histogram. Since the nuclei of the cells take the shades of blue after H&E staining, the area which
-has the maximum intensity of blue would be the area inside the nucleus. If the intensity with maximum frequency
-is not around the blue range, it is not the nucleus and that acts as the differencing factor while classifying nuclei
-against miscellaneous or poorly segmented region. Texture information contains the GLCM texture features [34].
-Among many GLCM texture features, we calculated four statistical texture features which are Contrast, Homogeneity,
-Correlation, and Energy of the nucleus surface. Texture, being a fundamental property of tissue surfaces, helps to
-differentiate between different types of cells such as epithelial, fibroblasts and lymph node cells. In papers [9], [11]
-and [12] authors described the shape and morphology features of different classes of nuclei. The considered shape and
-morphological features in this paper are the areas, major axis length, minor axis length, and eccentricity.
-3.3 Convolutional Feature Extraction
-CNN Architecture: The general CNN architecture first proposed by Yann LeCun et al. in their paper [35]. In CNN,
-unlike traditional machine learning approaches that take features as input to classify the data into categories, raw
-images are used as input. It works on the input images by learning filter weights and modify itself until convergence.
-The basic architecture of CNN comprises seven major layers, namely, Input image layer, Convolution layer, ReLU
-layer (Non-Linearity), Pooling (Local Max), Fully connected layer, Softmax Layer, and Classification Layer.
-An exhaustive theoretical account of CNN can be found in [36]. These layers, when combined in a pattern, create
-deeper networks with the increased number of hidden units. Deeper the network, a more exhaustive set of features
-could be extracted from the image [37]. However, this theory is highly subjective and depends majorly on the properties
-of the datasets. For example, the size of the image data should be large enough to be processed into a meaningful
-representation in case of architectures with great depths. Also, in a few cases, the number of parameters resulting
-due to large depths pose a major disadvantage in terms of computational load and efficiency of the algorithm. Hence,
-we did experiments to develop a custom three layer convolutional network to extract features from the nuclei dataset.
-We named it CNN 3Lfor ease of reference. Figure 2 shows the network in detail. After training the whole network
-for a set number of epochs, the final network yields the best set of features which are then further progressed to the
-classification layer. For the purpose of extracting DL features, it is preferred to extract features from the last layer
-located before the fully connected layer since the final convolution layer features are a more specific representation
-of the dataset images. Our proposed setup also has the flexibility to change the backbone architecture from shallow
-three-layer network to any number of layers.
-3.4 Fusion
-We get two set of features which are globally extracted from segmented nuclei (OL features) and automatically ex-
-tracted features that include both local and global information (DL features) of the image. We have concatenated
-these two sets along the axis that contained the feature vector of a sample and produced one combined set for further
-classification (Fig. 5). This exhaustive set of features are then used as input to the first MLP layer for the purpose of
-categorizing them into four classes.
-Figure 5: Diagram displaying fusion of OL and DL features
-7APREPRINT - FEBRUARY 22, 2022
-3.5 Classification
-For classification, we have used Multi-Layer Perceptron (MLP). This process is called Transfer Learning where CNN
-is used only for feature extraction while the next step of classification is performed by another machine learning
-algorithm such as MLP for multiclass classification. In this paper, we used the MLP network with one input layer, one
-hidden layer having ten nodes, and one output layer. Combination of features is fed as an input to the first MLP layer.
-Hidden layer used tansig as an activation function defined by
-tansig (n) =2
-(1 +exp(2n))1; (1)
-where nis our input vector to the second hidden layer. This activation function is faster than tanh , however numerical
-differences are small [38]. The output prediction scores from the output layer of MLP was given by softmax function
-[39] - [40] defined as:
-pj(^y(x)) =exp(^yj(x))P
-kexp(^yk(x)); (2)
-where ^yj(x)denotes the jthelement of output vector ^y(x)We performed 2-fold cross-validation test on our network
-to observe the efficacy of our model.
-4 Experimental Setup
-4.1 Database
-For our experiments, we have taken the database from [32]. This database has a total of 100 H&E stained histology
-images of size 500 by 500 pixels. The center coordinate location of each nucleus in each image has been provided
-as ground truth. Different types of nuclei are divided into sets belonging to four classes which are Epithelial nuclei,
-Fibroblast nuclei, Inflammatory nuclei and the rest of the small types are categorized as one class called ’miscella-
-neous’. We obtain subimages of size 27x27 extracted around the center coordinate locations and maintained them
-in four sub-folders segregated by class types. Four classes in the dataset have 7,722 Epithelial, 5,712 Fibroblasts,
-6,970 Inflammatory, and 2039 miscellaneous nuclei totaling up to 22,444 nuclei in 100 H&E stained histopathological
-images. The number of samples in each class are imbalanced which can cause classification results biased towards the
-majority class ( the class having the highest number of samples). A systematic study of the class imbalance problem
-in convolutional neural networks by authors in [41] have concluded through their research that the class imbalance
-problem, if not addressed may have a detrimental effect on classification performance. The influence of imbalance
-on classification performance increases with the size of the dataset. They also reported that in case of convolutional
-neural networks, the method that works best for eliminating class imbalance problem is oversampling with thresh-
-olding which opposed to other machine learning models, does not cause overfitting in CNN. Thus, we performed
-our experiments after balancing our dataset. We used adaptive synthetic sampling for eliminating class imbalance by
-synthetically creating new examples in the vicinity of the boundary between the two classes than in the interior of
-the minority class via linear interpolation between existing minority class examples [42]. So, after creating synthetic
-examples for minority classes (Fibroblast, Inflammatory, and miscellaneous) with respect to number of samples in
-majority class (Epithelial) we accumulated 29,771 total data points having 7,722, 7,275, 6,970, and 7804 samples
-in class 1 (Epithelial), class 2 (Fibroblast), class 3 (Inflammatory), and class 4 (Miscellaneous), respectively. After
-acquiring the balanced nuclei dataset, for the purpose of training, validation, and testing, we divided each class in
-the ratio 0.7:0.15:0.15, respectively. All the networks are evaluated on the testing set and performance metrics are
-reflected in Section V .
-4.2 CNN 3Land Parameter Settings
-We used the CNN framework with three convolutional layers and one fully connected layer as shown in Figure 2.
-Traditionally, CONV RELU layers are stacked, which are then followed by POOL layer. This pattern is repeated until
-we get a relatively small number of parameters from the image or when the optimal performance is achieved. The
-aim to stack up layers is to extract a more exhaustive set of features. The last fully-connected layer holds the output,
-such as the class scores. In our research work, experiments with a different number of layers yielded the optimal value
-of three convolution layers and one fully connected layer. We have used our knowledge about the problem, applied
-a heuristic approach to select parameters, and observed the outputs. Based on the outputs obtained, we tweaked
-the parameters. This process was repeated until we got the optimal set. The architecture was trained with different
-hyperparameters such as the number of convolutional layers and fully connected layers, learning rate and the number
-of epochs. The accuracy achieved at each instance was recorded and plotted as shown in figures 6a and 6b. The plots
-8APREPRINT - FEBRUARY 22, 2022
-show the accuracy values on the Y-axis corresponding to the number of epochs on X-axis for each type of architecture
-taken into consideration. Here, NCMFCdenotes N Convolutional layers and M Fully Connected layers. The two
-figures show observations recorded from two learning rates.
-(a) Accuracy at learning rate 1e-4
- (b) Accuracy at learning rate 1e-5
-Figure 6: Epochs v/s Accuracy graph at different hyperparameters
-From the experiments, we found out that neither stacking up more layers nor decreasing the learning rate showed a
-positive impact on the performance of the network. A learning rate of 1e4with three convolutional layers and 1
-fully connected layer the accuracy recorded at epoch number 100 was the highest (refer figure 6a). Increasing the
-learning rate further to 1e3for CNN 3Ldecreased the accuracy to 61.77% at epoch 70 and further down to 59.41%
-at epoch 100.
-For training the final optimal network (CNN 3L), we normalized our dataset images by subtracting the mean image of
-the training set from all training set images before using them as input vectors. If we don’t scale our input training
-vectors, the feature values distribution range would likely be different for each feature, and thus the corrections in each
-dimension after each epoch through learning rate will differ for each dimension. This might cause overcompensation
-(very high variance from mean weight) in weight of one dimension while undercompensating (very low variance from
-mean weight) the another. This creates a non-ideal situation since it might get difficult to center the weight space. This
-will also affect the time efficiency as it will travel too slow to get to a better maxima.
-In our architecture CNN 3L, We followed 5 x 5, 100 channel, 3 x 3, 50 channels, and 3 x 3, 100 channels for each
-convolutional layer respectively. We experimented with different filter window size and numbers of filters and ob-
-served little to no improvement in the performance, for example, experiment with filter numbers 32, 64 and 128 on
-subsequent three convolutional layers of the optimized CNN 3Lachieved the F1-score of 0.7907. There could be an
-infinite number of permutations to choose network parameters from. We performed numerous experiments to decide
-on CNN 3Las our optimal network, however finding a theoretical justification is out of the scope of this work. Convo-
-lution layer is followed by ReLU layer. There is no change in the dimension of the output of this layer. Next is the Max
-pooling layer with size 2 and stride 1. Since, our network depth is less so, keeping stride 1 in Max pooling layer was a
-design choice to avoid loss of information. Total number of learnable parameters were only 118,000 as compared to
-heavier networks like AlexNet ( 56,000,000), VGG16 ( 134,000,000), VGG19( 139,000,000), ResNet50( 23,000,000),
-InceptionV3( 22,000,000), and DenseNet121( 7,000,000). After fixing the network architecture, first branch 1 of the
-network was trained. Trained Branch 1 (CNN layers) were tested on the test set and the results ( OUTPUT -1 ) are
-accumulated in Table 1. Applying transfer learning approach [43], we used MLP for final classification outcome
-(OUTPUT - 2) instead of 1-of-4 classification output from a softmax layer. Trained CNN backbone has been used
-as fixed feature extractor. The approach is stage-wise learning and the output 2 observations are recorded in Table 2
-under the column named as ”stage-wise”.
-4.3 End-to-End Training
-We performed end-to-end training of our three layer CNN architecture (CNN 3Lto show the performance of our net-
-work when the learning happened from end-to-end rather than stage wise. By end-to-end learning, we mean that both
-CNN network (CNN 3L) and MLP network were trained together using the intermediate outputs of CNN 3Las inputs to
-the MLP network. So, MLP network received two sets of input features, activated features from the last Max pooling
-layer of CNN 3Land the other from the pre-calculated handcrafted features. These two set of features were concate-
-nated before giving them as input to the MLP layers. At the end of the training, two outputs were accumulated, one
-9APREPRINT - FEBRUARY 22, 2022
-from the CNN 3L1 of 4 softmax outputs and another from the MLP layer classification. The diagram showing the
-architectural setup is shown in Figure 2. We performed two experiments in different settings.
-1. Branch 1 (CNN layers) of the network (refer Figure 2) was trained keeping branch 2 (MLP layers) inactive
-by turning off the switch and the results of 1-of-4 classification layer of CNN was recorded.
-2. Both branch 1 and branch 2 were trained simultaneously to obtain two outputs, one from CNN layers
-(OUTPUT-1) and second from MLP layers (OUTPUT-2) (refer Figure 2). The switch was connected in
-this case.
-We have reported the output of MLP classification of combined feature set (OUTPUT-2) in Table 2.
-5 Results and Discussion
-The aim of this work is to highlight the different outcomes of classifying the database with different settings. To
-represent the outcomes in order of increasing classification performance we have divided our results in the following
-categories.
-1. Classification output of only deep learning features from softmax layer of CNN 3L
-2. Classification results of combined feature set using MLP (O LConvNet) - Stagewise.
-3. Classification results of combined feature set using MLP (O LConvNet) - End-to-end.
-5.1 Results
-Data imbalance has been eliminated using the method described in Section 4.1. We evaluated the proposed model
-on the basis of F1-scores and multiclass AUC. Multiclass AUC is calculated using the prediction scores given by the
-softmax function described in (2).
-Table 1 shows the comparative classification performance of MLP and softmax CNN networks, separately. OL features
-classified the nuclei classes using MLP classifier (Branch - 2 of Fig. 2) while DL features were propagated along the
-fully connected and classification layers of CNN networks (Branch - 1 of Fig. 2). To visualize the network flow
-in both OL classification and deep learning classification, connection through the switch in Fig. 2 was broken to
-create two separate branches where, both the branches were trained mutually exclusively. We compared the OL
-features performance with CRImage [44] (Table 1) which also calculates features after the segmentation of the nuclei
-using basic thresholding, morphological operations, distance transform, and watershed. CRImage also calculates
-statistical features from the segmented nucleus but lacks visual, shape and texture features. Besides statistical features,
-incorporation of histopathologically deduced features such as nuclei size, shape, area, color, and texture hold direct
-relevance to the dataset and therefore the absence of such features prevents CRImage to perform well on the dataset.
-So, from the observations reported in Table 1, we deduced that our OL features, though only a small number, performed
-better than [44]. Deep models CNN 3L, AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 when
-tested (without OL features) on the dataset produced F1-score and AUC better than OL features by a huge margin due
-to exhaustive feature set produced after convolution operations. These features are non-interpretable but have been
-known to perform quite well in classification tasks. Hence, Table 1 reports the individual performance of OL features
-and DL features and proves the statements made in the ’Introduction’ Section about the need to shift from traditional
-feature engineering to convolutional feature learning.
-Next, we trained the network in Fig. 2 stage wise. Features from trained deep learning networks from the previous
-experiment were concatenated with OL handcrafted features. Then, the second stage of the training of concatenated
-features was done by MLP classifier. The results obtained on test dataset after MLP training was then reported as
-combined features classification performance. End to end training followed stagewise experiments to analyze the
-effect of the performance of our network when trained in two ways. We have shown the difference through F1-score,
-multiclass AUC and cross-entropy loss in Table 2 which reports performance of classification on a 2-fold cross-
-validation experiment. The obtained results from stage-wise training, as expected, showed improvement in F1-Score
-and Multiclass AUC in comparison to individual results obtained after classifying with only DL features and only OL
-features (Table 1). While there is only a 2% increase in F1-score and 1% increase in AUC score in case of CNN 3L,
-a marked improvement has been recorded in case of deeper pre-trained architectures. Whereas, in the case of end-2-
-end training, no improvement in the performance metrics has been observed in any of the backbone networks. Also,
-the higher loss value recorded in the case of end-2-end approach reflects decreased performance in comparison to
-the stage-wise approach. These cross-entropy loss values highlight that the joint loss propagation after the shared
-layer (concatenation layer) might have affected the overall performance of the model. The additional OL feature
-10APREPRINT - FEBRUARY 22, 2022
-Table 1: Comparison between methods stratified by classifier
-MethodPrecision Recall F1-Score Multiclass AUCBackbone Classifier
-Only Object Level MLP 0.5154 0.5156 0.5135 0.7857
-CRImage [44] SVM (RBF kernel) * * 0.4880 0.6840
-CNN 3L Softmax 0.8043 0.8046 0.8040 0.9441
-AlexNet Softmax 0.8280 0.8281 0.8216 0.9386
-VGG16 Softmax 0.8693 0.8699 0.8689 0.9757
-VGG19 Softmax 0.8575 0.8581 0.8578 0.9701
-ResNet50 Softmax 0.8900 0.8893 0.8892 0.9799
-InceptionV3 Softmax 0.8164 0.8175 0.8175 0.9538
-DenseNet121 Softmax 0.8784 0.8706 0.8706 0.9756* data
-unavailable
-set concatenated simultaneously during training on the shared concatenation layer did not improve the discriminative
-property of the DL features and hence, the subsequent MLP layers performed in the same way as the fully connected
-and softmax layers of DL models.
-Fig. 7 show the ROC curves obtained after end-2-end training on all four networks. The subfigures 7a, 7b, 7c, 7d,
-7e, 7f, and 7g shows the ROC curves of all four classes with respect to seven backbone networks, discriminated
-through four colors. The figure also mentions the AUC value for individual classes. Micro and Macro average of
-four ROC curves from each class show similar value because our dataset is balanced. The micro-average method
-calculates the sum of true positives, false positives, and false negatives for different sets whereas, in Macro-average
-method, the average of the precision and recall is taken into account. Micro Average is preferred when there is a
-class imbalance problem. The Figure also labels AUC values for each class. After recording the values we observed
-a dip of 2% to 4% for class 2 (Fibroblast) and 3 (Inflammatory), whereas, the AUC values for classes 1 (Epithelial)
-and 4 (Miscellaneous) are comparable across all four networks. The decrease in performance can be attributed to
-the number of samples which are relatively less for class 2 (7275) and class 3 (6970) than class 1 (7722) and class
-4 (7804). Also, in the case of Fibroblasts (class 2), the long spindle-shaped cell barely has a visible nucleus which
-prevents segmentation algorithms to detect the nucleus area effectively. Consistent class-wise performance across all
-four backbones and high AUC are some of the other observations deduced from Figure 7.
-2-fold cross validation performance metrics with CNN 3Lalong with deep architectures AlexNet H, VGG16 H, VGG19 H,
-ResNet50 H, InceptionV3 H,and DenseNet121 H, where H stands for Hybrid, are reported in Table 3 and Figure 8. The
-OLConvNet metrics recorded in Table 3 are stagewise observations from OUTPUT - 2 of the network obtained after
-combined feature set testing. We compared our results with Softmax CNN + SSPP (Standard Single Patch Predictor)
-and Softmax CNN + NEP (Neighboring Ensemble Predictor) [32] architectures used for classification of nuclei from
-the nuclei dataset cited by [32]. The authors in this article worked on the theory that the pixel of interest is likely
-the center of the nucleus and using this theory they formulated the classification algorithm by spatially constraining
-the high probability pixels in the vicinity of the centers of nuclei. They proposed two algorithms called Neighboring
-Ensemble Predictor (NEP) and Standard Single Patch Predictor(SSPP) which when coupled with SC-CNN (Spatially
-Constrained- CNN) produced the classification results as mentioned in Table 2. The drawback of their method was
-in building a complex target specific model which lead to low classification performance. Their CNN architecture is
-custom but they didn’t test their methodology on deeper pre-trained architectures which might have performed better
-with or without their elaborate model. Further comparison with superpixel descriptor [15] and CRImage on the same
-dataset show that both the methods performed relatively poor and that our method exhibited a higher performance
-regardless of the backbone architecture used. The motivation behind devising Superpixel descriptor was to distinguish
-the area with the different histologic pattern. This descriptor lacks direct features related to the visual appearance of
-the nucleus, like color, texture, and shape, thus yielded lower classification performance. Whereas, CRImage [24] only
-calculates segmented nuclei features. Segmented nuclei features are insufficient to classify complex nuclei patterns
-due to either weak staining or presence of overlapping boundaries. This prevents CRImage to perform well in this
-dataset. We have shown the same through our only OL features classification results in Table 1. Figure 8 reports
-comparative class-wise classification performance of all the methods. F1-score of Miscellaneous class show a marked
-jump in values obtained from O LConvNet and an improvement of more than 47% is recorded between the highest
-and lowest performing algorithms which are ResNet50 and CRImage, respectively. For Epithelial and Fibroblast, the
-11APREPRINT - FEBRUARY 22, 2022
-(a) CNN 3LTest ROC
- (b) AlexNet Test ROC
-(c) VGG16 Test ROC
- (d) VGG19 Test ROC
-(e) ResNet50 Test ROC
- (f) InceptionV3 Test ROC
-(g) DenseNet121 Test ROC
-Figure 7: ROC curves from four backbone networks using O LConvNet (end-2-end learning)
-12APREPRINT - FEBRUARY 22, 2022
-Table 2: Performance parameters from stage wise and end-2-end learning experiments (OUTPUT - 2)
-Method BackboneStagewise End-2-End
-F1-score AUC loss F1-score AUC loss
-OLConvNetCNN 3L 0.8243 0.9587 0.1211 0.8084 0.9500 1.2750
-AlexNet H 0.9542 0.9903 0.0915 0.8359 0.9600 1.0860
-VGG16 H 0.9569 0.9923 0.1036 0.8731 0.9700 0.9407
-VGG19 H 0.9546 0.9879 0.1221 0.8675 0.9700 1.1213
-ResNet50 H 0.9677 0.9973 0.0272 0.8892 0.9799 0.7335
-InceptionV3 H 0.9618 0.9963 0.0309 0.8175 0.9538 1.0795
-DenseNet121 H 0.9616 0.9961 0.0318 0.8706 0.9756 0.7972
-class wise performance of CNN 3Lwas marginally lesser than SSPP but, a difference of 4% to 7% was recorded
-with NEP whereas, in case of DL models used in O LConvNet, the F1-scores have been consistently better than all
-the algorithms used for comparison. Same can be interpreted from Figure 8 where the classwise performance of
-OLConvNet backbones is consistently better than contemporary algorithms.
-Table 3: Comparative Performance parameters for Nucleus Classification
-Method Backbone Precision Recall F1-Score Multiclass AUC
-Softmax CNN + SSPP [32] CNN * * 0.7480 0.8930
-Softmax CNN + NEP [32] CNN * * 0.7840 0.9170
-Superpixel Descriptor [15] - * * 0.6870 0.8530
-CRImage [44] - * * 0.4880 0.6840
-OLConvNetCNN 3L 0.8241 0.8245 0.8243 0.9587
-AlexNet H 0.9578 0.9577 0.9578 0.9953
-VGG16 H 0.9611 0.9610 0.9610 0.9960
-VGG19 H 0.9544 0.9548 0.9546 0.9879
-ResNet50 H 0.9676 0.9678 0.9677 0.9973
-InceptionV3 H 0.9618 0.9618 0.9618 0.9963
-DenseNet H 0.9616 0.9617 0.9616 0.9961* data
-unavailable
-5.2 Observations and Discussions
-The experiments performed in this study gave some interesting points for discussion. Formally, whenever we per-
-formed deep learning based classification tasks, our focus generally remained on improving the classification perfor-
-mance of the architecture by fine-tuning or transfer learning. Transfer learning is generally used in the case when there
-are fewer samples of similar data. In our case as well, samples were not enough for deep architectures to generalize
-well on the dataset. Moreover, pre-trained weights of discriminative nuclei types to transfer learn on our database was
-also not available. Also, the intention to not use pre-trained publically available ImageNet weights was because our
-dataset is very dissimilar to ImageNet. Hence, we chose to build our own CNN layers (CNN 3L) and fine-tune them to
-produce optimized performance parameters.
-DL features from CNN 3Land OL features representing visual and structural properties of nuclei were concatenated
-to produce a combined feature set which consequently improved classification results. F1-Score and multiclass AUC
-values recorded in Table 1 reflected the individual performance of the traditional handcrafted OL features with MLP
-classifier and DL features with Softmax classifier whereas, F1-Score and multiclass AUC values in Table 3 show
-the performance of the O LConvNet network with combined feature sets. These observations reflect that even if the
-OL feature set was very small (total 9) relative to CNN 3Lfeature length (48,400) , a marked difference between 1-
-of-4 softmax classification (OUTPUT 1 of Fig. 2) and O LConvNet classification (OUTPUT 2 of Fig. 2) reflect the
-applicability and importance of including object-specific features. Further, the high performance metric values in Table
-3, exhibited by deeper pre-trained architectures ( AlexNet H,V GG 16H,V GG 19H,ResNet 50H,InceptionV 3H,
-13APREPRINT - FEBRUARY 22, 2022
-Figure 8: Comparative results for nucleus classification stratified with respect to class label
-DenseNet 121H) shows that increasing depth of the network although increase the capability to extract discriminative
-DL features and hence better F1-score and AUC from CNN 3L(as reflected in Table 7) but, the discriminative ability of
-the DL feature set is enhanced many folds when combined with OL features. This statement can be verified from Table
-2 which shows the increase of 10% in F1-score and AUC values of these pre-trained architectures. However, training
-OLConvNet jointly for classification (end-2-end ) ( Table 2) using a combined feature set did not affect F1-score
-and AUC values. This is likely because we trained the network with the combined loss of CNN classification layer
-and MLP output layer. The combined cross-entropy loss affected the performance of the joint network. Therefore,
-from Table 2 we observe that the loss values are higher in end-2-end training in comparison to stage-wise training.
-Hence, stagewise 2-fold cross-validation results are preferred over end-2-end classification. The results were also
-suggestive of the fact that the classification performance has most certainly increased quite dramatically as the levels
-of convolution layers increased from just three layers in CNN 3Lto 121 layers in DenseNet121. In all the cases, though,
-our hypothesis remained true and our architectural setup provided the room to make modifications in the configuration
-of backbone networks.
-The question that can be raised at this point is why to use CNN 3Lwhen we are getting better performance with deeper
-architectures. In support of CNN 3L, when we look in Table 2 and observe cross-entropy loss of all backbones in
-stagewise learning, we see that CNN 3Lhas comparable loss value with deeper networks but, the number of parameters
-and the training time taken by AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121 shown in Table
-4 is much larger than CNN 3L. This opens up the discussion for the need of using an optimal number of layers to
-achieve satisfactory performance by an application instead of very deep CNN models. To further strengthen our point,
-we know that in deep learning, a number of successful early and recent state of the art architectures such as AlexNet,
-VGG16, VGG19, , ResNet50, InceptionV3, and DenseNet121 etc., were developed to increase the abstractness in
-extracting deep and better quality feature maps, primarily for the purpose of classification. However, the standard
-datasets used to test these networks were real life natural images.
-Medical data is a completely different modality that has high variations in features from the same class. Therefore,
-the state of the art deep network models on such datasets do not perform well and suffer from high validation and
-testing loss. Another main reason for their failure is a scarcity of labeled data volume in the medical image domain.
-To cope up with these limitations, every new model that is being developed for medical image data classification
-is function specific and does not address the global problems in the domain. Most of the recent literature in the
-classification of structures in histology images do not use raw images for deep learning models. A fair amount of pre
-and post processing of data is required to enhance the performance which consequently, hampers the generalization
-capability of the model. So, instead of building novel models with less or limited applicability in the cell nuclei image
-classification problem, it seemed better to change the way these models are used. With simple architectural change
-and introduction of visually and structurally relevant handcrafted feature set, through experiments, we have established
-gain in performance values. Our model is flexible in a way that any deep model can be fitted in the architecture to
-extract deep features. Another highlight of our work is to show how the addition of a handful of basic OL handcrafted
-14APREPRINT - FEBRUARY 22, 2022
-features can bring a notable change in the final classification output. We do not need to design special descriptors or
-use complex handcrafted features to complement deep learning features. Instead, a small number of discriminative
-OL features in case of nuclei classification like color, texture, and shape can enhance the discriminative capability of
-the neural network classifier.
-While we agree that the architecture is simple, we first need a fine-tuned and flexible model that scales well with
-the number of options of novel models available today. More than that, we need a compact model with less training
-time ( a comparatively shallow convolutional network ) that fairly works well and give comparable results with deeper
-architectures. We compared the time taken by each of the methods to train in Table 4. We observed that CNN 3L
-took the least amount of time and very huge differences were noted when compared with ResNet50, InceptionV3, and
-DenseNet121. Time may be regarded as an insignificant paradigm with advanced computer systems available today.
-However here, it is important to mention that, we trained our algorithms using three NVIDIA GeForce GTX 1080
-Ti GPUs with 11GB RAM each, in parallel. This much computing power is not still really common in most of the
-places and, also it becomes difficult to train heavy deep networks in light mobile applications or hand-held systems.
-These much computing resources are impossible to accommodate in lighter applications as of yet and hence, shallow
-networks that work fairly well for general diagnostic procedures can help in reducing space and time constraints for
-such systems. In this research, we are dealing with time sensitive application where the quick delivery of classification
-results are important to help pathologists to proceed for further analysis in cancerous tissues and diagnose cancer as
-soon as possible. Besides time, we would also like to highlight that our architecture CNN 3Lis not using pre-trained
-weights of ImageNet which has several other advantages such as, applications can use the custom size of their dataset
-directly without the need of resizing it to conform according to the size of ImageNet data. This is beneficial at times
-where very large or very small images may lose quite a significant amount of details when scaled. While one may
-argue in this case that we could have trained all DL backbones from scratch without the need of using pre-trained
-weights. However, these deep networks require a certain minimum size of the image (48 x 48 in case of VGG16 and
-VGG19, 197 for resNet50 and 221 for DenseNet121) at the input layer to train. Hence, the limitation of these state
-of the art deep learning architectures with complex datasets is too much to ignore. Additionaly, for training such deep
-networks from scratch, we would need a very large number of dataset samples for the networks to learn efficiently.
-Table 4 summarizes the time is taken and the number of trainable parameters used by CNN 3L, AlexNet, VGG16,
-VGG19, ResNet50, InceptionV3, and DenseNet121 to train the dataset.
-Table 4: Time and trainable parameters comparison between backbone architectures
-Method No. of parameters Time (seconds)
-CNN 3L 118 thousands 303
-AlexNet 56 millions 3304
-VGG16 134 millions 1408
-VGG19 139 millions 1420
-ResNet50 23 millions 18951
-InceptionV3 22 millions 13314
-DenseNet121 7 millions 24515
-Hence, a simple architectural setup whose components can be modelled as per the dataset requirement and which uses
-the combination of OL features and shallow, yet incorporating the properties of DL model - CNN 3Lin case of complex
-dataset such as ours has proved to be a better approach than the traditional OL models or trending DL algorithms, alone
-that do not allow changes in the architecture and require specific configurations to work well. It is important to note
-that there are no standard datasets yet in case of histological nuclei images so, comparing various methods mentioned
-in the literature on only one database does not guarantee to give expected results. According to the free lunch theorem,
-which is most certainly applicable in case of medical image databases, there is no global algorithm that could be
-developed to give good results across all kinds of histopathological data. Each experiment is conducted with different
-dataset acquired by the research team themselves or from the pathologists, whose property changes with the location
-of the disease. Their results are validated using different performance metrics. Hence, standard datasets and ground
-truth in the case of complex histopathological cancer images is a current challenge in this field of research.
-6 Conclusion
-The knowledge about the cell of origin of a tumor may benefit doctors to treat the tumor more effectively since a correct
-classification will greatly increase the biological understanding of carcinogenesis. Using this motivation in this paper,
-15APREPRINT - FEBRUARY 22, 2022
-we have built our classification model by emphasizing on a hybrid and flexible model that can incorporate the two
-wide domains of features, the age-old traditional object-level features such as intensity, texture, and shape features and
-recent deep learning based features. While object-level features have proved their efficiency in various domains and
-purposes of biomedical image processing, including cancer-based disease recognition and classification, the current
-trend has drastically shifted towards using various deep learning methods. Our work tried to highlight through the
-results that using only deep learning might not work in case of all datasets. Therefore, a need to develop a shallow
-yet effective architecture such as CNN 3Lincorporated in our proposed skeleton called O LConvNet, that could robustly
-combine the benefits of object level features in this study, motivated our work. Moreover, to guarantee a wide range of
-applicability, a model that is easy to understand, and deploy is required, also, which has the flexibility to incorporate
-both deeper, more efficient networks like AlexNet, VGG16, VGG19, ResNet50, InceptionV3, and DenseNet121, and
-shallow, light model like CNN 3Lso that the O LConvNet can be adapted to a wider range of applications. The results
-were encouraging and our network performed better than the recent state of the art implementation on the same dataset.
-The future work could be to incorporate better algorithms that combine the best of both the worlds i.e. traditional object
-level and deep learning features. Approaches can differ with better classifiers as well. In conclusion, our method opens
-the possibility of further research in developing more robust nuclei classification models that can scale well on all kind
-of datasets.
-7 Acknowledgments
-This research was carried out in Indian Institute of Information Technology, Allahabad and supported by the Ministry
-of Human Resource and Development, Government of India. We are also grateful to the NVIDIA corporation for
-supporting our research in this area by granting us TitanX (PASCAL) GPU.
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-18

txt/2203.01944.txt

@@ -1,954 +0,0 @@
-1
-A multi-stream convolutional neural network for
-classification of progressive MCI in Alzheimer’s
-disease using structural MRI images
-Mona Ashtari-Majlan, Abbas Seifi, Mohammad Mahdi Dehshibi
-Abstract — Early diagnosis of Alzheimer’s disease and
-its prodromal stage, also known as mild cognitive impair-
-ment (MCI), is critical since some patients with progressive
-MCI will develop the disease. We propose a multi-stream
-deep convolutional neural network fed with patch-based
-imaging data to classify stable MCI and progressive MCI.
-First, we compare MRI images of Alzheimer’s disease with
-cognitively normal subjects to identify distinct anatomical
-landmarks using a multivariate statistical test. These land-
-marks are then used to extract patches that are fed into
-the proposed multi-stream convolutional neural network to
-classify MRI images. Next, we train the architecture in a
-separate scenario using samples from Alzheimer’s disease
-images, which are anatomically similar to the progressive
-MCI ones and cognitively normal images to compensate for
-the lack of progressive MCI training data. Finally, we trans-
-fer the trained model weights to the proposed architecture
-in order to fine-tune the model using progressive MCI and
-stable MCI data. Experimental results on the ADNI-1 dataset
-indicate that our method outperforms existing methods for
-MCI classification, with an F1-score of 85.96%.
-Index Terms — Alzheimer’s disease, Brain-shaped map,
-Convolutional Neural Network, Multivariate statistical test,
-Transfer learning.
-I. INTRODUCTION
-ALZHEIMER’S disease (AD) is a progressive neurode-
-generative disorder that is one of the leading causes
-of dementia in the elderly. According to [1], this disorder
-affects over 30 million people worldwide. Early diagnosis
-of this disease and its prodromal stage, also known as mild
-cognitive impairment (MCI), is crucial since 10% to 15% of
-MCI patients progress to AD, which is classified as progressive
-MCI (pMCI) [2].
-As AD advances, several brain regions develop structural
-deformation and atrophy [3]. Structural Magnetic Resonance
-Imaging (sMRI) is one of the most widely employed neu-
-roimaging tools for predicting this disorder through identifying
-brain atrophy [4] (see Fig. 1). In addition to sMRI (referred
-to as MRI in this paper), non-invasive biomarkers such as (1)
-M. Ashtri-Majlan is with the Department of Computer Sci-
-ence, Universitat Oberta de Catalunya, Barcelona, Spain (e-mail:
-[email protected]).
-A. Seifi is with the Department of Industrial Engineering, Amirkabir
-University of Technology, Tehran, Iran (e-mail: aseifi@aut.ac.ir).
-M. M. Dehshibi is with the Department of Computer Science,
-Universitat Oberta de Catalunya, Barcelona, Spain (e-mail: moham-
-[email protected]).demographic information ( e.g., age and education) [5], and (2)
-cognitive test scores [4] can also be used to provide possible
-discriminative information for diagnosing AD in the early
-stages. Several studies [4], [6], [7], [8], [9] have addressed
-the MCI-to-AD conversion issue using neuroimaging methods
-in conjunction with the biomarkers.
-(a)
- (b)
-Fig. 1. MRI samples of (a) Cognitively Normal (CN) and (b) AD
-classes from Alzheimer’s Disease Neuroimaging Initiative database
-(ADNI-1) [10]. In order to demonstrate the subtle brain atrophy, we
-highlighted the affected regions.
-Conventional methods in medical image processing typ-
-ically use prior knowledge to segment brain images into
-Regions of Interest (ROI) [11] or V oxel-Based Morphometry
-(VBM) [12] to predict AD. While these methods can classify
-stable MCI (sMCI) and pMCI, the focus was mainly on
-Alzheimer’s disease. Deep learning algorithms, on the other
-hand, have made it possible for researchers to combine feature
-extraction, dimensionality reduction, and classification in an
-end-to-end way. These algorithms also outperform conven-
-tional methods in identifying AD patterns because they can
-discover hidden representations among multiple regions of
-neuroimages. Hence, these models have gained prominence
-in analysing Alzheimer’s disease.
-In this paper, we propose a multi-stream convolutional
-neural network (CNN) for classifying sMCI and pMCI, which
-is fed with patch-based imaging data extracted using a novel
-data-driven technique. To accomplish so, we first use the
-multivariate T2 Hotelling test to compare MRI images of AD
-and cognitively normal (CN) individuals in order to identify
-distinct anatomical landmarks. Following that, the statistical
-test is performed on textural and statistical features extractedarXiv:2203.01944v1  [eess.IV]  3 Mar 20222
-from MRI images. These landmarks are then used to generate
-191919patches, with each landmark serving as the
-centre of the patch. Finally, the extracted patches are fed
-into the proposed multi-stream CNN to classify MRI images.
-To compensate for the lack of pMCI training data, we first
-train the proposed architecture using AD/CN images that are
-anatomically similar to the pMCI ones. Then, we transfer the
-weights of the trained model to the proposed architecture in
-order to fine-tune the model using pMCI and sMCI data. The
-contribution of this study is two-fold:
-1) Rather than utilising non-rigid registration to identify
-anatomical landmarks in the brain, we propose employ-
-ing rigid registration to reduce computational complexity
-and eliminate morphological structure deformations that
-could cause inherent errors in the classification step.
-Therefore, we partition each MRI image during the
-anatomical landmark detection phase and perform the
-T2 Hotelling test on these partitions to capture subtle
-anatomical differences, to account for more information,
-and to reduce the impact of potential errors caused by
-inter-subject brain shape variations. Finally, the identi-
-fied statistically significant landmarks centres from the
-partitions are used as anchoring forces for selecting the
-patches fed into the proposed multi-stream CNN.
-2) We propose using transfer learning to classify
-pMCI/sMCI classes in training the proposed architecture
-to overcome the complexity of learning caused by the
-subtle structural changes in MCI brains compared to AD
-and CN. In the Ablation study, we also demonstrate the
-importance of employing transfer learning. Furthermore,
-we address intrinsic errors caused by inter-subject brain
-shape differences by conducting experiments to deter-
-mine an ideal image patch size in order to feed to the
-proposed CNN model.
-The rest of this paper is organised as follows: Section II
-surveys the previous studies. Section III describes the proposed
-method. Experimental results are given in Section IV. Finally,
-the paper is concluded in Section V.
-II. L ITERATURE REVIEW
-Mild cognitive impairment (MCI) is a stage of cognitive
-decline that occurs between the predicted cognitive loss as-
-sociated with normal ageing and the more severe decline
-associated with dementia. MCI may result in a higher level
-of developing dementia caused by Alzheimer’s disease if the
-anatomical changes in the brain are proactive. Progressive
-MCI differs from stable MCI in the progression of functional
-connectivity values over time. However, classifying pMCI and
-sMCI patients is challenging due to the subtle anatomical
-differences in the brain [13]. The four conventional feature
-extraction approaches usually mentioned in the literature for
-classifying pMCI and sMCI are voxel-based, slice-based, ROI-
-based, and patch-based [14], although they are not entirely
-mutually exclusive. In this section, before surveying recent
-advances in deep learning-based methods for classifying pMCI
-and sMCI, we will briefly review these four approaches by
-discussing the advantages and disadvantages of each group.The voxel-based techniques [15], [16], [17] use the voxel
-intensity values from all neuroimaging modalities. Although
-voxel-based techniques are simple to implement, they typically
-require spatial co-alignment of the input image to standard 3D
-space and suffer from high dimension feature space compared
-to available sample numbers. Ortiz et al. [18] used the t-
-test algorithm to partition the brain area into 3D patches
-in order to address the mentioned drawbacks and eliminate
-non-significant voxels. The patches were then used to train
-an ensemble of deep belief networks, and a voting scheme
-was used to make the final prediction. However, as mentioned
-by [19], there is an inherent over-fitting challenge with voxel-
-based techniques.
-The sliced-based techniques [20], [21] extract slices from
-the 3D neuroimaging brain scan by projecting the sagittal,
-coronal, and axial to the 2D image slices. Indeed, because
-non-affected regions and normal slices must be chosen as the
-reference distribution, they cannot account for the disease and
-may be considered an anomaly [22]. Furthermore, choosing
-separate 2D slices may neglect the spatial dependencies of
-voxels in adjacent slices due to inter/intra anatomical variances
-in the brain images [14]. However, sliced-based techniques
-allow for the usage of a broader range of conventional and
-deep learning-based approaches. For instance, different pre-
-trained deep learning models on ImageNet, such as DenseNet,
-VGG16, GoogLeNet, and ResNet, can be fine-tuned by 2D
-slices to classify AD from CN [19]. In [21], researchers
-extracted features from MRI image slices using a pre-trained
-2D CNN and fed the extracted feature sequence to a recurrent
-neural network (RNN). The RNN was in charge of determining
-the relationship between the sequence of extracted features
-corresponding to MRI image slices. However, sliced-based
-techniques are computationally expensive due to the use of
-additional learnable parameters which cannot directly benefit
-from transfer learning.
-ROI-based techniques consider brain regions that have
-been predefined physically or functionally [23], [8], [24].
-These methods use spatial information such as automated
-anatomical labelling [25] and diffusion-weighted imaging in
-MRI to extract features. The prominent regions that have
-been considered by almost all ROI-based feature extraction
-studies on AD prediction are the hippocampus, amygdala,
-and entorhinal. However, one of the advantages of employing
-ROI-based approaches is like a double-edged sword which can
-be a disadvantage because ROI identification requires expert
-human expertise. Furthermore, these techniques are considered
-time-consuming due to the need for non-linear registration
-and brain tissue segmentation. There is also the possibility
-of information loss because the abnormal region may spread
-from a single ROI to multiple ROIs.
-Patched-based approaches [26], [27] partition the entire
-brain into multiple patches from which numerous feature
-vectors are extracted. The extracted patches include shapes,
-texture, and volume features generated from distinct brain
-regions or specific patterns. This computational approach
-eliminates the need for manual ROI identification, and makes
-it possible to use landmark-based patch extraction or other
-discriminative patch-based biomarkers [8]. However, selecting3
-useful patches from a complete image is problematic, mainly
-due to increasing computational complexity when a non-
-rigid registration approach is used. Researchers have used
-rigid registration or embedded the registration step into deep
-learning-based methods to address this challenge [28]. There
-is another issue with patched-based approaches related to
-multiple instance learning. Although this challenge is almost
-addressed in classifying AD and CN by leveraging patch
-relationships [29], [30], the difficulty in classifying pMCI
-in Alzheimer’s disease is not still resolved. This issue is
-linked to the bag’s labelling in classifying pMCI from sMCI,
-where a bag can get a negative label even though it contains
-informative anatomical landmark(s), but it cannot meet the
-majority rule.
-Convolutional neural networks (CNNs) are a popular deep
-learning approach for classifying Alzheimer’s disease. Islam
-and Zhang [31] proposed three 2D CNNs to generate three
-distinct MRI views. Each CNN in their architecture comprised
-three convolutional layers and four dense blocks, where the
-final decision was made by majority vote. Oh et al. [32] pro-
-posed a convolutional autoencoder-based approach for the AD
-and CN classification task, addressing the pMCI data limitation
-with transfer learning. Liu et al. [8] suggested a coarse-to-
-fine hierarchical ensemble learning method for simultaneous
-hippocampus segmentation and Alzheimer’s disease classi-
-fication that employed a multi-task deep CNN architecture
-and 3D densely connected CNNs. In this method, an MRI
-image is first divided into multiple slices, and then a pre-
-trained deep neural network is used to extract features from
-the slices. The coarse predictions were then used in ensemble
-learning to obtain refined results for all slices. Ebrahimi et
-al. [21] extracted a sequence of features from 2D MRI slices
-using a pre-trained ResNet18 [33], which they subsequently
-trained a temporal convolutional network and several types
-of RNNs to classify AD/CN. Zhao et al. [24] introduced aregion ensemble model with three sequential sub-networks
-to account for a global feature map derived from the entire
-brain and regional feature maps extracted using a segmentation
-model. The feature representations were fused in their method,
-and the classification was performed using an attention-based
-technique. Researchers employed a data-driven technique to
-select informative patches in [34], which resulted in specific
-landmark localisation in brain MRI images. Each landmark
-patch was then fed into the CNN models, which produced the
-final classification result using the maximum voting strategy.
-P¨olsterl et al. [35] proposed the dynamic affine feature map
-transform, an auxiliary module for CNNs that dynamically
-incites or represses each feature map of a convolutional layer
-based on both image and tabular biomarkers. A more detailed
-overview of deep learning algorithms for Alzheimer’s disease
-classification can be found in [36], [14], [37].
-III. P ROPOSED METHOD
-Given a dataset of NsamplesD=f(xi; yi)gN
-i=1, with xi2
-Rdxandyi2Rdy, our goal is to train a multi-stream deep
-convolutional neural network H(x) =E[YjX=x]to classify
-sMCI and pMCI by minimising the cross-entropy between the
-class labels and the softmax output as in Eq. 1
-p(yijx;w; b) =exp(xTwi+bi)P
-j2dyexp(xTwj+bj): (1)
-where wandbare the network’s weights and bias terms, re-
-spectively. In this study, we use the baseline 1.5T T1-weighted
-MRI images of subjects from the ADNI-1 dataset [10], where
-the input image Xhas a size of dx= 185155150and is
-labelled by dy=f0;1g. The output label Yconsists of two
-probability values in the [0;1]range withH(xi) = 0 if the i-th
-sample belongs to the sMCI class and H(xi) = 1 otherwise.
-It has been shown [38] that in the early stages of
-Alzheimer’s disease, only certain brain regions are subject
-(a)
-Fig. 2. The schematic of the proposed multi-stream convolutional neural network.4
-to morphological changes caused by the disease. Therefore,
-we conduct a statistical test to identify these informative
-landmarks in the MRI images and extract Lpatches from
-each MRI image xi=fsi;jgL
-j=1, with si;j2R191919.
-The proposed data-driven approach for extracting patches from
-the MRI image, on which a preprocessing step has been
-performed, is described in the following subsections, followed
-by the details of the multi-stream CNN. Figure 2 shows the
-schematic of the proposed multi-stream architecture.
-A. Preprocessing
-We pre-process the MRI images to use them in the proposed
-method. There are four steps in the preprocessing phase: (1)
-anterior commissure-posterior commissure correction using
-the 3D Slicer software1; (2) intensity inhomogeneity correction
-using N4ITK [39], an enhanced version of nonparametric
-nonuniform normalisation; (3) skull stripping using a pre-
-trained U-Net2to remove both the skull and the dura; and
-(4) rigid registration, which involves linearly aligning MRI
-images to the Colin27 template and resampling them to a size
-of155185150with a resolution of 111 mm3. Figure 3
-shows a sample of MRI image from ADNI-1 dataset on which
-the preprocessing is performed.
-(a)
- (b)
- (c)
- (d)
-Fig. 3. The visual representation of the preprocessing steps for an
-MRI sample. (a) A raw MRI image, (b) the MRI image with anterior
-commissure-posterior commissure correction (c) the MRI image with
-intensity inhomogeneity correction, and (4) skull stripped MRI image.
-The yellow line in (a) and (b) depicts the anterior commissure-posterior
-commissure line.
-B. Anatomical landmark detection
-We must first identify the anatomical locations in the brain
-that are most influenced by the disease before we can classify
-sMCI and pMCI patients. As a result, we randomly divide
-samples from AD and CN individuals into the train, validation,
-and test sets with sizes of 0:7N,0:1N, and 0:2N,
-respectively, where Nis the total number of samples. Then,
-we select M= 0:7N(i.e., the training set) and propose a
-novel data-driven landmark detection method in which MRI
-images are partitioned into 555patches. As mentioned
-in [40], [41], when a patient is diagnosed with Alzheimer’s
-disease, several regions in the brain are subject to anatomical
-degeneration. At the pMCI stage, the same regions undergo
-anatomical changes, but the degeneration is not as severe as
-those seen at the onset of Alzheimer’s disease. With respect to
-this fact, we use identical anatomical locations for classifying
-sMCI and pMCI patients.
-1http://www.slicer.org/
-2https://github.com/iitzco/deepbrainEach partition is then represented by a 29-dimensional
-feature vector. This feature vector includes the Gray-Level Co-
-Occurrence Matrix (GLCM) [42], Structural Similarity Index
-Measure (SSIM) [43], Mean Square Error (MSE), entropy, and
-the mean and standard deviation of the partition voxels. To
-extract the GLCM elements of the feature vector, we generate
-six GLCM matrices with three adjacency directions, namely
-horizontal, vertical, and in-depth, each of which is associated
-with two distance values. Then, we extract contrast, corre-
-lation, homogeneity, and entropy, from each GLCM matrix
-resulting in a total of 24 elements. The Colin27 template [44]
-is used as the reference image for measuring SSIM and MSE
-at each patch location. Finally, we apply the multivariate T2
-Hotelling statistical test [45] to generate a brain-shaped p-
-value map (see Fig. 4). Algorithm 1 details the steps of
-generating the brain-shaped p-value map.
-Fig. 4. A brain-shaped p-value map in which top 50 landmark locations
-are represented by spheres in a gradient colour from red to blue, with p-
-values ranging from 0 to 0.001. Each p-value is paired with a landmark
-(lx; ly; lz).
-After obtainingPset, we exclude landmarks with a spatial
-Euclidean distance of less than 15 to reduce the redundancy of
-overlapped adjacent patches to identify the most discriminative
-anatomical landmarks in the brain. The top 50 landmarks with
-the lowest p-values are then chosen (see Fig. 4).
-The registration and landmark detection steps in Algo-
-rithm 1 are affected by the MRI image partitioning size, i.e.,
-555. In the case of selecting a smaller partition size,
-the lack of adequate morphological variations could lead to
-discarding informative landmarks. In contrast, larger partition
-sizes cause intrinsic physiological differences to eclipse subtle
-disease-related changes.
-Therefore, after obtaining the top 50 landmarks, we sample
-27 3D image patches with a 333displacement around
-the centre of each landmark to increase the size of patches
-to191919with two intentions: (i) compensating for
-regions that may unintentionally be discarded in anatomical
-landmark detection, and (ii) providing sufficient morphological
-structures for each stream of the proposed CNN to construct
-a discriminative latent space.5
-Algorithm 1: Generating brain-shaped p-value map.
-Input : AD=fxAD
-1; xAD
-2;


; xAD
-Mg
-CN=fxCN
-1; xCN
-2;


; xCN
-Mg
-K= 34;410, which is the total number of
-patches with a size of 555.
-Output:P: A set of p-value , forming the brain-shaped
-p-value map.
-Step 1: Partitioning
-1VAD Partition( AD);
-//VAD=fp1;1;


; p1;K;


; pM;1;


; pM;Kg.
-2VCN Partition( CN);
-//VCN=fq1;1;


; q1;K;


; qM;1;


; qM;Kg.
-Step 2: Feature extraction & T2 Hotelling test
-3forj 1toKdo
-4 fori 1toMdo
-5 fAD
-i;j Feature-Extraction( pi;j);
-6 fCN
-i;j Feature-Extraction( qi;j);
-7 end
-8 pvalue j Hotelling-Test( fAD
-M;j; fCN
-M;j);
-//fM;jis a M29matrix,
-representing extracted features
-form the jthpatch.
-9end
-10P Sort( pvalue; asc);
-// Each pvalue is paired with a
-landmark (lx; ly; lz).
-11returnP
-C. Multi-stream classifier architecture
-We propose a multi-stream CNN architecture with L
-streams, each fed with the patch si;jextracted from the input
-image xiand centred on the identified landmark location
-(lx;j; ly;j; lz;j). We construct the patch si;jwith a size of
-191919, surrounding the corresponding landmark location,
-in order to better represent morphological variations in the
-MRI images. The local spectral-spatial feature is extracted
-from each 3D image patch by each stream of the proposed
-CNN architecture.
-As depicted in Fig. 2, the proposed multi-stream CNN has
-L= 50 streams, with an identical structure. Each stream has
-five convolutional layers (Conv), followed by a rectified linear
-unit (ReLU) activation function. The convolutional layers have
-32, 64, 64, 128, and 128 333convolution filters,
-respectively. After Conv2, Conv4, and Conv5, we consider
-batch normalisation and 222max-pooling layers. There
-are three fully connected layers with 128, 64, and 8 units at the
-end of each stream, which are followed by a dropout layer with
-a ratio of 0.4 to prevent overfitting. Although the architecture
-of all 50 streams is the same, their weights are tuned and
-updated separately, where the input patches for each stream
-are randomly selected to avoid the unintentional bias towards
-ordering streams. We concatenate the outputs of 50 streams
-and add a dropout layer with a ratio of 0.6 to fuse the locally
-extracted spectral-spatial features. Before passing the featurevector into the softmax function for the final classification,
-we add three fully connected layers with 64, 64, and 32 units,
-respectively.
-IV. E XPERIMENTS
-A. ADNI-1 dataset
-In this study, we use the baseline 1.5T T1-weighted MRI
-images of subjects from the ADNI-1 dataset [10]. The vol-
-umetric 3D MPRAGE protocol is used to acquire sagittal
-T1-weighted MRI images with an in-plane spatial resolution
-of1:251:25 mm2and 1.2 mm thick sagittal slices. The
-imaging dataset contains baseline images from 695 partic-
-ipants including 200 Alzheimer’s disease, 231 cognitively
-normal, 164 progressive MCI, and 100 stable MCI. Figure 5
-shows four samples from this dataset, and Table I presents the
-demographic and clinical information of subjects in ADNI-1.
-(a)
- (b)
- (c)
- (d)
-Fig. 5. Four samples from ADNI-1 dataset [10] (a) AD, (b) CN, (c) pMCI,
-and (d) sMCI
-B. Architecture details and Evaluation metrics
-The proposed architecture is implemented using Python
-based on the Keras package3, on a computer with Intel(R)
-Core(TM) i7-4790K @4.00 GHz CPU and 16G RAM. We
-trained the network using Adam optimiser [46] with the first
-momentum of 0.9 and the second momentum of 0.999. The
-initial learning rate and the constant for numerical stability
-are set to 103and106, respectively. We set the maximum
-number of training epochs to 40 and used a mini-batch-size of
-5 at each iteration, where the training data was shuffled before
-each training epoch. There are two other hyper-parameters
-which are the number of streams Land the patch size.
-We evaluate the performance of the proposed ar-
-chitecture with the number of streams in the range
-f10;20;30;40;50;60gand the size of patches in the range
-f9;11;15;19;23gusing the validation set from the AD and
-CN classes.It is worth noting that because there are two
-hyperparameters, one must be fixed in order to generate the
-other. Figure 6 shows the accuracy of the proposed multi-
-stream CNN as a function of these two hyper-parameters.
-While increasing the number of streams improves classifi-
-cation accuracy (see Fig. 6a), it also increases computational
-complexity. We can also observe in Fig. 6b that a larger patch
-size ( i.e.,232323) shows a performance drop when
-compared with the 191919patch size. This is owing
-to the fact that enlarging the patch size includes tissues in that
-3https://github.com/fchollet/keras6
-TABLE I
-DEMOGRAPHIC AND CLINICAL INFORMATION OF SUBJECTS IN ADNI-1. V ALUES ARE REPORTED AS MEAN STANDARD DEVIATION .
-Class Male/Female Age ADAS CDR-sb FAQ MMSE NPI-Q
-AD 103/97 75.64 7.71 13.02 5.23 4.39 1.6 13.16 6.71 23.31 2.03 3.44 3.27
-CN 119/112 76.155 4.99 28.51 4.89 0.03 0.12 0.13 0.59 29.09 0.98 0.36 0.95
-sMCI 66/34 75.44 7.27 22.93 5.78 1.24 0.62 1.65 3.00 27.65 1.70 1.45 2.40
-pMCI 97/67 74.54 7.05 17.67 5.14 1.87 0.96 5.64 5.15 26.62 1.71 2.30 3.11
-ADAS: Alzheimer’s Disease Assessment Scale
-CDR-sb: Clinical Dementia Rating ‘sum of boxes’
-FAQ: Functional Activities QuestionnaireMMSE: Mini-Mental State Examination
-NPI-Q: Neuropsychiatric Inventory Questionnaire
-(a)
- (b)
-Fig. 6. The accuracy of the proposed multi-stream CNN classifier as a
-function of (a) the number of streams, and (b) the patch size.
-patch of the brain whose intrinsic morphological similarity
-is dominant to subtle changes that aid in the early diagnosis
-of the disease. Therefore, instead of discriminating the subtle
-changes induced by the disease’s development in the early
-stages, the network discriminates irrelevant tissues.As a result,
-to establish a trade-off between computational complexity and
-accuracy, we set the number of streams Lto 50 and the patch
-size to 191919and preserve these values in the rest of
-the experiments.
-To evaluate the performance of the proposed architecture,
-we use the metrics as in Eq. 2.
-ACC =TP+TN
-TP+TN+FP+FN:
-SEN =TP
-TP+FN;
-SPE =TN
-TN+FP;
-F1score =2SENSPE
-SEN + SPE:(2)
-where TP,TN,FP, and FN denote true positive, true
-negative, false positive, and false negative, respectively. We
-also report area under receiver operating characteristic curve
-(AUC).
-C. Experimental results
-To assess the performance of the proposed architecture,
-we performed three steps: (1) training the proposed multi-
-stream CNN with MRI images of AD and CN subjects, (2)
-transferring weights from the trained model in Step 1 to the
-identical architecture and fine-tuning it with the data related
-to sMCI and pMCI patients, and (3) adding biomarkers asan auxiliary modality to examine the reciprocal influence of
-spectral-spatial features and biomarkers.
-(1) Classification of AD and CN subjects : To train the pro-
-posed multi-stream CNN, we randomly select 70% of the MRI
-patches from the two classes of AD and CN as the training
-set, 20% as the test set, and 10% as the validation set. We
-also compare the trained model in this step with 10 additional
-approaches, including regions of interest (ROI) [47], V oxel-
-Based Morphometry (VBM) [12], and five deep learning-based
-methods. We used the FAST algorithm [48] to segment brain
-MRI images into three different tissues for the ROI and VBM
-comparison: White Matter (WM), Gray Matter (GM), and
-Cerebrospinal Fluid (CSF). We followed the implementations
-described by the researchers for the deep learning-based
-approaches.
-For the RIO-based method, we align the anatomical auto-
-mated labelling template [25] with the native space of each
-MRI image. This template contains 116 predefined regions
-in which the extracted GM tissue volumes are normalised
-using the summation of GM, WM, and CSF volumes. This
-normalised tissue volume is used as the representative feature
-for the MRI images. For the VBM method, we use affine reg-
-istration with 12 degrees of freedom to align MRI images with
-the Colin27 template [44] in order to extract the GM density
-as the representative feature. To reduce the dimensionality of
-this feature vector, we perform the t-student statistical test on
-the extracted features from AD and CN subjects and chose
-GM densities with p-values less than 0.001.
-Finally, we classify AD and CN using these two feature
-representations using a linear support vector machine
-(SVM) [50] with soft-margin and a multilayer perceptron
-(MLP). The MLP comprises two hidden layers, with 13
-and 15 neurons, and a binary output layer with a logistic
-activation function. We train these classifiers using the 5-fold
-cross-validation strategy. To ensure a fair comparison for
-MCI conversion prediction, we train the VBM and ROI
-models on AD/CN and test them on an independent test set
-of pMCI/sMCI.
-(2) Classifying sMCI and pMCI using transfer learning :
-The availability of MRI images for sMCI and pMCI patients
-is less than that of AD because the symptoms of Alzheimer’s
-disease are not as severe at these stages. As a result, many
-patients may not be asked to get an MRI test. Furthermore,
-structural changes in MCI brains caused by dementia may be7
-TABLE II
-RESULTS OF CLASSIFYING AD/CN AND P MCI/ SMCI USING THE PROPOSED MULTI -STREAM CNN S ALONG WITH 10ADDITIONAL APPROACHES .
-MethodAD vs. CN (%) pMCI vs. sMCI (%)
-ACC SEN SPE F1-score AUC ACC SEN SPE F1-score AUC
-ROI + SVM 70.30 68.51 67.50 67.83 79.39 59.47 66.86 68.90 67.87 60.99
-VBM + SVM 82.84 82.54 80.50 81.34 90.39 70.08 81.48 67.07 73.58 74.38
-ROI + MLP 73.08 70.00 71.19 70.59 77.52 58.75 70.00 66.04 67.96 53.93
-VBM + MLP 83.85 78.33 85.45 81.74 89.92 70.00 72.00 78.26 75.00 73.93
-Shmulev et al. [49] - - - - - 76.00 70.00 88.00 77.97 86.00
-DM2L[5] 91.09 93.50 88.05 90.69 95.86 76.90 82.43 42.11 55.74 77.64
-H-FCN [6] 90.30 96.50 82.40 85.08 95.10 80.90 85.40 52.60 65.10 78.10
-HybNet [28] 91.90 82.40 94.50 91.50 96.50 82.70 57.90 86.60 69.39 79.30
-Zhao et al. [24] - - - - - 85.90 50.00 91.60 64.68 85.40
-Proposed architectureLbiomarkers 97.54 95.54 99.40 97.43 99.38 69.21 68.56 93.15 78.98 77.35
-Proposed architecture 97.78 95.59 99.82 97.66 99.97 79.90 75.55 99.70 85.96 94.39
-very subtle compared to CN and AD, making the convergence
-of the proposed multi-stream CNN challenging. To overcome
-these limitations, we first trained our model using data from
-CN and AD MRI images and fine-tune the model with data
-gathered from sMCI and pMCI patients.
-Since the pre-trained streams of the proposed architecture
-are designed to extract structural features that correspond to
-AD, we freeze the initial layers and only re-train the last
-three fully connected layers. To fine-tune the model, we used
-70% of the pMCI and sMCI MRI patches and tested the
-fine-tuned model with the remaining data.
-(3) Unleashing the impact of biomarkers : In addition to
-MRI images, non-invasive biomarkers such as demographic
-information and cognitive test scores [4] can also be used to
-provide potentially discriminatory information for diagnosing
-Alzheimer’s disease in its early stages. We propose adding
-biomarkers as an auxiliary modality to the architecture in order
-to evaluate the reciprocal influence of spectral-spatial features
-and biomarkers. Therefore, we introduce an additional input
-stream to the proposed architecture to incorporate numerical
-values offAge, ADAS, CDR-sb, FAQ, MMSE, NPI-Q g(see
-Table I). However, a subset of the MRI images in the ADNI-
-1 dataset does not include biomarkers, resulting in missing
-values when incorporating them into the proposed CNN archi-
-tecture. We use k-Nearest Neighbour (kNN) as a preprocessing
-step to handle missing values. First, we execute the kNN
-with k= 6 (analogous to 6 biomarkers) on the available
-data. Then, for those entries that do not have a biomarker,
-we fill in the value with the average values of the knearest
-neighbours. Finally, we incorporate these biomarkers into the
-architecture at the ‘Concatenate’ layer while preserving the
-learning parameters as described in Section IV-B.
-Table II shows the performance of the proposed architec-
-tures compared with 10 other approaches. As shown in Ta-
-ble II, the proposed multi-stream CNNs achieve considerably
-better F1-scores than both conventional and deep learning-
-based approaches. While the biomarkers can improve the
-classification performance, the lack of large-scale data is
-a barrier to incorporating them into the proposed method.
-Furthermore, as previously stated, the cognitive test scores
-of individuals suffering from various types of MCI differ
-slightly. This subtle difference could explain why the multi-stream architecture combined with biomarkers performs poorly
-in distinguishing pMCI and sMCI when compared with the AD
-and CN classification. We also plot the ROC curves in Fig. 7
-for classifying both AD vs. CN and pMCI vs. sMCI.
-(a)
- (b)
-Fig. 7. ROC curves of the proposed multi-stream architecture, the
-proposed architecture integrated with biomarkers, ROI, and VBM-based
-methods for (a) AD/CN classification, and (b) pMCI/sMCI classification.
-In the case of classifying AD and CN, Fig. 7a shows that
-the proposed architecture achieves an AUC of 99.97%, outper-
-forming ROI, VBM, and multi-stream architecture integrated
-with biomarkers, which achieve AUCs of 79.39%, 90.39%,
-and 99.38%, respectively. Furthermore, as demonstrated in
-Fig. 7b, when we use transfer learning, the fine-tuned model
-can provide appropriate discriminative information to classify
-pMCI and sMCI subjects. This evidence can also convey that
-AD and pMCI have structural deformations similar to the
-extracted features from various landmarks in different streams
-of the proposed architecture.
-The results in Table II show that the proposed multi-stream
-architecture combined with biomarkers is slightly less efficient
-than the multi-stream CNN in distinguishing pMCI from
-sMCI when compared with the AD and CN classification. To
-highlight these subtle differences and better comprehend the
-reciprocal influence of spectral-spatial features and biomark-
-ers, we have visualised the class-discriminative localisation
-map and the latent space in Fig. 8 using Grad-CAM [51] and
-t-SNE [52], respectively. Figure 8a shows an example of a
-class-discriminative localisation map for the last convolutional
-layers in the proposed multi-stream CNN in which the salient
-regions of each brain patch have been identified so that the
-classifier could distinguish between AD/CN and pMCI/sMCI8
-(a)
- (b)
-Fig. 8. (a) An example of a class-discriminative localisation map for the last convolutional layers in the proposed multi-stream CNN using Grad-
-CAM [51]. Each stream has 128 filters. However, in order to keep the publication page limits, we only illustrate five out of 50 patches, each
-representing five filters. The colourbar represents the relevance of each pixel in the Grad-CAM, where the red colour has the highest importance
-in the trained network’s final decision and the blue colour has the least importance. (b) Visualisation of the feature space using t-SNE [52] for the
-classification of AD/CN and pMCI/sMCI with and without biomarkers.
-with high potential. Furthermore, we have observed in Fig. 8b
-that introducing biomarkers as an auxiliary modality causes the
-clusters ( i.e., AD/CN and pMCI/sMCI) to become tighter with
-a degree of overlap than without the biomarkers. This evidence
-can explain why the proposed multi-stream CNN without
-biomarkers performs marginally better, where the latent space
-provides for clean cluster separation.
-D. Ablation study
-In the ablation study, we conduct two sets of experiments
-to better understand the efficacy of the proposed multi-stream
-CNN for the classification of pMCI in Alzheimer’s disease.
-We therefore examine:
-— Contribution of transfer learning : In this experiment,
-instead of using transfer learning, we test the baseline perfor-
-mance when the model is directly trained on pMCI/sMCI data.
-We have 100 and 164 sMCI and pMCI samples, respectively,
-and use the same data split as in the transfer learning (70/30)
-for a fair comparison. The other learning parameters are
-preserved as described in Section IV-B. The evaluation metrics
-(i.e., ACC: 63.53%, SEN: 100%, SPE: 0.0%, and F1-score:
-77.70%) reveal that the model cannot converge to classify
-pMCI/sMCI classes without using transfer learning. In fact,
-the trained model predicts the same optimal output regardless
-of the input, with the average answer minimising loss.
-— Contribution of multi-stream architecture : In this ex-
-periment, instead of the proposed multi-stream CNN, we use
-a single stream CNN to which 3D patches are fed. The
-model is trained on the AD/CN dataset and fine-tuned using
-pMCI/sMCI data with the same data split used in the previous
-experiments. The evaluation metrics ( i.e., ACC: 56.96%, SEN:
-67.35%, SPE: 40.00%, and F1-score: 66.00%) show that the
-model is unable to converge to classify pMCI/sMCI classes,
-and the trained model is predicting random class for all the
-data points regardless of the input. These metrics highlighttwo main facts: (1) with the multi-stream architecture, the
-model can build a more discriminative latent space as a result
-of focusing on the landmarks identified in the anatomical
-landmark detection phase, which are more likely to be signs
-of Alzheimer’s disease. However, with a single-stream ar-
-chitecture, the model cannot distinguish between anatomical
-abnormalities caused by the disease and those caused by the
-morphology of the brain. Indeed, the model’s sensitivity and
-specificity are inadvertently changed due to the morphological
-similarity of the inputs; (2) input patches to the single-stream
-architecture predominantly capture local information of the
-image, and the global relationship between different landmark
-locations is no longer taken into account.
-V. C ONCLUSION
-Alzheimer’s disease is the most common type of dementia,
-resulting in memory impairment and cognitive decline. Mild
-cognitive impairment is a prodromal stage of AD, also known
-as the transition stage. MCI patients either progress to AD or
-remain at the same stage over time. Therefore, it is critical to
-distinguish between progressive MCI and stable MCI in early
-stages to prevent rapid progression of the disease.
-In this study, we have proposed a method for classifying
-pMCI and sMCI patients using MRI images. The proposed
-method consists of two main steps. (1) We have developed
-a novel data-driven approach based on the multivariate T2
-Hotelling statistical test to identify anatomical landmarks in
-MRI images and generate a brain-shaped p-value map. Each
-landmark is paired with a 3D coordinate, allowing us to extract
-patches of 191919. (2) We have proposed a multi-stream
-deep convolutional neural network in which each stream is
-fed by one of the patches. This multi-stream CNN employed
-transfer learning to classify pMCI and sMCI patients using the
-ADNI-1 dataset. We assessed the proposed method in three
-experimental steps and demonstrated the significance of our
-contributions to transfer learning and multi-stream architecture9
-in the Ablation study. We have performed several experiments
-to evaluate the efficiency of the proposed architecture based
-on the best practices. Experimental results have shown that
-our method outperformed existing approaches, particularly in
-the classification of MCI patients. Thus, this method can
-assist practitioners to expand investigating on various diseases
-associated with structural atrophy.
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-Mona Ashtari-Majlan received her Master’s de-
-gree in Health Systems Engineering from Amirk-
-abir University of Technology, Tehran, in 2021.
-She is a PhD candidate in computer science
-at Universitat Oberta de Catalunya, Spain. Her
-area of interest includes Biomedical Image Pro-
-cessing, Computer Vision, and Deep Learning.
-Abbas Seifi is a professor of Industrial Engi-
-neering and Management Systems at Amirkabir
-University of Technology in Iran. He did his
-BASc and MASc in Industrial Engineering at
-Sharif University of Technology. He received his
-PhD in Systems Design Engineering from the
-University of Waterloo in Canada and worked
-there as a postdoctoral research associate for
-over 2 years. His teaching and research interests
-include optimisation and simulation of various
-operational research problems, data driven op-
-timisation, data science, machine learning and system dynamics.
-Mohammad Mahdi Dehshibi received his PhD
-in Computer Science in 2017 from IAU, Iran. He
-is currently a research scientist at Universitat
-Oberta de Catalunya, Spain. He was also a
-visiting researcher at Unconventional Computing
-Lab, UWE, Bristol, UK. He has contributed to
-more than 60 papers published in scientific jour-
-nals and international conferences. His research
-interests include Affective Computing, Medical
-data processing, Deep Learning, and Unconven-
-tional Computing.

txt/2203.10094.txt

@@ -1,497 +0,0 @@
-K. López de Ipiña  et al., "Selection of entropy based features for the analysis of the Archimedes' spiral applied to 
-essential tremor,"  2015 4th International Work Conference on Bioinspired Intelligence (IWOBI ), 2015, pp. 157 -
-162, doi: 10.1109/IWOBI.2015.7160160 . 
-_________________________________________________________________________________________  
-Selection  of entropy  based features  for the  analysis  of 
-the Archimedes’  spiral  applied to essential  tremor  
- 
-K. López  de Ipiña,  M. Iturrate,  P. Calvo,  B. Beitia,  J. 
-Garcia -Melero  
-Universidad  del País  Vasco/Euskal Herriko  Unibertsitatea  
-{karmele.ipina, mikel.iturrate, itziar.gurruchaga,  
-mariablanca.beitia,  joseba.garcia}@ehu.eus  
- 
-A. Bergareche,  P. De  la Riva,  J.F. Marti -Masso,  
-BioDonostia  Health  Institute,  Donostia,  Spain  
-{jesusalberto.bergarecheyarza,  patricia.delarivajuez,  
-josefelix.martimasso}@osakidetza.eus  M. Faundez -Zanuy,  E. Sesa -Nogueras,  J.Roure  
-Escola  Universitaria  Politècnica  de Mataró  (UPF),  
-Tecnocampus  
-{faundez,  sesa, roure  }@tecnocampus.cat  
- 
-J. Solé-Casals  
-Data  and Signal  Processing  Group.  University  of Vic – 
-Central University of Catalonia  
-[email protected]  
- 
-Abstract—: Biomedical systems are regulated by interacting  
-mechanisms that operate across multiple spatial and temporal  
-scales  and produce  biosignals  with  linear  and non-linear  
-information inside. In this sense entropy could provide a useful  
-measure about disorder in the system, lack of information in  
-time -series and/or irregularity of the signals. Essential tremor  
-(ET) is t he most common movement  disorder,  being 20 times  
-more  common  than  Parkinson’s  disease,  and 50-70%  of this 
-disease cases are estimated to be genetic in origin. Archimedes  
-spiral drawing is one of the most used standard tests for clinical  
-diagnosis. This work, on selection of nonlinear biomarkers from  
-drawings and handwriting, is part of a wide -ranging cross study  
-for the diagnosis  of essential  tremor  in BioDonostia  Health  
-Institute. Several entropy algorithms are used to generate non - 
-linear feayures. The automatic analysis system consists of several  
-Machine Learning  paradigms.  
- 
-Keywords — Permutation entropy; Essential tremor; Automatic  
-drawing  analysis;  Archimedes’  spiral;  Non-linear  features;  
-automatic  selection  of features  
-I. INTRODUCTION  
-Biomedical  systems  are regulated  by interacting  
-mechanisms that operate across multiple spatial and temporal  
-scales  and produce  biosignals  with linear  and non-linear  
-information  inside.  Output  variables  of real systems  often  have  
-complex fluctuations that are not only due to noise but also  
-contain  information  about  the intrinsic  dynamics  and the 
-underlying system. In all cases the dynamics’ global aspects  
-can be somehow captured by classic linear methods, but the  
-different  approaches  are not equivalent  to discern  all the 
-relevant physical details [1,2]. In this sense the measurement  
-of non -linear features such as the system entropy are essential  
-and useful tools to analyse the system stage. The analysis of  
-system entropy provides not only the probability distributions  
-of the possible  state of a system but  also the information  
-encoded in it [1]. However the applicability of entropy based  
-methodologies  depends  on particular  characteristics  of the 
-data,  such as stationarity,  time series  length,  variation  of the parameters, level of noise contamination, etc., and important  
-information  may be codified  also in the temporal  dynamics,  an 
-aspect which is not usually taken into account [1,3]. Time  
-series generated by biological and biomedical systems most  
-likely contain  deterministic and stochastic components [4].  
-Classical methods of  signal  and noise  analysis  can quantify  the 
-degree  of regularity  of a time series  by evaluating  the 
-appearance of repetitive patterns, but most such methods only  
-model linear components without introducing any information  
-about non -linearity, irregularities or stochastic components.  
-This complex  information  could  be essential  when  subtle  
-changes are anal ysed. Massimiliano Zanin et al [1] present a  
-review  based  on biomedical  applications  which  includes  
-analysis  about  EEG,  anesthesia,  cognitive neuroscience  or 
-heart rhythms. Among biomedical applications, the related to  
-neurological diseases are a challenge due to their variability  
-and impact in society. Essential tremor is one of the most  
-common.  
-Essential  tremor  is a condition  that affects  individuals  
-worldwide, being 20 times more common than Parkinson’s  
-disease.  The prevalence  of essential  tremor  (ET)  in the 
-western world is of about 0.3 -4.0%, 40 years of old males and  
-females  are affected  more  or less equally  with an incidence  of 
-23.7 per 100,000  people  per year.  Studies  in the elderly  
-suggest that prevalence in these  patients ranges between 3.9%  
-and 14.0%. 50 -70% of essential tremor cases are estimated to  
-be genetic in origin [5]. Essential tremor presents itself as a  
-rhythmic tremor (4 –12 Hz) that occurs only when the affected  
-muscle  is exerting  effort.  The amplitude  of the tremor  
-increases its variability with regard to age  but there is no  
-gender predilection. Physical or mental stress could make the  
-tremor worse and the prevalence of Parkinson's disease, in  
-people with essential tremor is greater than in the general  
-population.  Parkinson's  disease  and parkinsonism can  also 
-occur simultaneously with essential tremor. With regard to  
-symptoms  hand  tremor  predominates  (as it does in Parkinson’s  
-disease), and  occurs in nearly all cases,  followed  by head  
-tremor,  voice  tremor, neck,  face, leg,  tongue  and trunk  tremor.  K. López de Ipiña  et al., "Selection of entropy based features for the analysis of the Archimedes' spiral applied to 
-essential tremor,"  2015 4th International Work Conference on Bioinspired Intelligence (IWOBI ), 2015, pp. 157 -
-162, doi: 10.1109/IWOBI.2015.7160160 . 
-_________________________________________________________________________________________  
-ET is characterized  by postural  and kinetic  tremor  which  often  
-maximally  affects  the hands.  PD and ET can appear  in 
-individuals  of the same  family  [5]. 
-The clinical  hallmark and  earliest  manifestation  of the 
-disorder is essential to manage and palliate the symptoms. All  
-these symptoms lead to impaired performance in everyday  
-activities. Approaches to the early diagnosis of ET have in the  
-past few years made significant advances in the deve lopment  
-of reliable  clinical  biomarkers.  Despite  the usefulness  of 
-biomarkers, the cost and technology requirements involved  
-make it impossible to apply such tests to all patients with  
-motor  troubles.  Given  these  problems,  non-invasive  intelligent  
-techniques of diagnosis may become valuable tools for early  
-detection  of disorders.  Non-technical  staff in the habitual  
-environments of the patient could use these methodologies,  
-without altering or blocking the patients' abilities, as speech  
-analysis, han dwriting or drawing analysis involved in these  
-techniques is not perceived as a stressful test by the patient.  
-Moreover,  these  techniques  are very low -cost and do not 
-require extensive infrastructure or the availability of medical  
-equipment.  They  are thus  capable  of yielding information  
-easily, quickly, and inexpensively [6 -8]. It is well established  
-that handwritten tasks can be used for diagnosis of essential  
-tremor. In this sense Archimedes’s spiral is one of the most  
-used standard  tests in clinical  diagnosis  [14].  
-In the past, the analysis  of handwriting  had to be 
-performed  in an offline  manner.  Only  the writing  itself  
-(strokes on a paper) were available for analysis. Nowadays,  
-modern capturing devices, such as digitizing tablets and pens  
-(with  or without  ink) can gather  data without  losing  its 
-temporal  dimension.  When  spatiotemporal  information  is 
-available, its analysis is referred as online. Modern digitizing  
-tablets  not only gather  the x and y coordinates  that describe  the 
-movement of the writing device as it changes its position, but  
-it can also collect other data, such as the pressure exerted by  
-the writing device on the writing surface, to the azimuth, the  
-angle of the pen in the horizontal plane, the altitude, the angle  
-of the pen with respect the vertical axis [9]. This gives the  
-possibility to  analyze  not only static  (“off -line”.)  but also 
-dynamic  (“on-line”)  features  [10].  Figure 1. . The Archimedes’ spiral drawing performed by an  
-individual  with essential  tremor.  
-This work is part of a wide -ranging cross study for the  
-diagnosis of essential tremor.  The general transversal study is  
-focused to characterize ET (Biodonostia Health Institute) in a  
-study  based  on families  with identified  genetics  loci. 
-Archimedes’s spiral h as been selected for the evaluation of  
-nonlinear  biomarkers  from drawings and handwriting.  The 
-presence of integrated features of other diseases such as stress  
-is also analyzed. In the next sections not only classical linear  
-features  static  and dynamics  but also non-linear  features  based  
-on several  entropy  algorithms  will be analyzed.  In that 
-biomarker selection, automatic methodologies will be used.  
-Finally  an automatic  analysis  system  based  on Machine  
-Learning  paradigms  measures  the quality  of the selec ted 
-features.  
-II. MATERIALS  
-The acquisition is carried out using an Intuos Wacom 4  
-digitizing  tablet.  The pen tablet  USB  [11] captures  the 
-following information. The tablet acquired 100 samples per  
-second including the spatial coordinates ( x, y), the pressure,  
-and azimuth and altitude angles. Using this set of dynamic  
-data, further information can be inferred, such as acceleration,  
-velocity,  instantaneous  trajectory  angle,  instantaneous  
-displacement,  tangential  acceleration,  curvature  radius,  
-centripetal accele ration, etc [12].  The database BIODARW  
-consists of 21 control people (CR) and 29 ET people with  
-identified  genetics  loci and register  of electrophysiological  test 
-(EPT) and fMRI. The test consists of a line, the Archimedes’  
-spiral drawing and handwriting w ith dominant hand and non - 
-dominant hand. Table 1 summarizes the features of the group  
-with ET with regard to EPT, diagnosis and demography [13].  
-In this work  Archimedes’  spiral  is used (Figure  1). The 
-database presents variability with regard to: tremor frequency,  
-amplitude and pattern, diagnosis scale and demography data  
-(age and gender).  From the original database, a subset of the  
-samples  of Archimedes’s  spiral  is selected.  Moreover  from  the 
-group wit h ET, only the samples of the hand with essential  
-tremor  are selected.  Thus  this sub-database  BIODARWO  
-consists of 51 samples:  27 samples of the control group and  
-24 samples  of the group  with ET. 
-III. METHODS  
-A. Feature  extraction  
-The research  presented  here is in the nature  of a 
-preliminary experiment; its aim is to define thresholds for a  
-number of biomarkers related to handwriting. It forms part of  
-a broader study focused on early ET detection. Feature search  
-in this work aims at pre clinical evaluation so as to formulate  
-useful  tests for ET diagnosis  [5,13,14].  
-1) Linear  features  
-In this study, we aim at automatically distinguishing of  
-handwriting between an ET patient and a healthy subject by  
-analyzing  different  linear  features  (LF) and their variants  
-(max, min, mean and median) in handwriting: time, spatial  
-components, pressure, speed, acceleration, zero crossing rate,  
-and spectral components  for on-surface  and in-air signals.  
-K. López de Ipiña  et al., "Selection of entropy based features for the analysis of the Archimedes' spiral applied to 
-essential tremor,"  2015 4th International Work Conference on Bioinspired Intelligence (IWOBI ), 2015, pp. 157 -
-162, doi: 10.1109/IWOBI.2015.7160160 . 
-_________________________________________________________________________________________  
-i 2) Non-linear  feature:  entropy  
-Entropy is  a measure  of disorder  in physical  systems,  and n−m  
-Am(r) = (n − m)−1 ∑ Am(r), (5) 
-also a basic  quantity  with multiple  field-specific  
-interpretations.  It has  been  associated with disorder, state - 
-space volume, or lack of information [1,2,15,16]. When try to  
-analyze information  content,  the Shannon entropy is  often  
-considered as the classic, foundational and most natural one  
-[3,4,17]. Richman et al., analyze that entropy, as it relates to  
-dynamical systems, is the rate of information production [18].  
-On the one hand some authors points that the calculation of  
-entropy  usually  requires  very long data sets that can be 
-difficult or impossible to obtain mainly for biomedical signal.  
-On the other  hand methods for estimation of the entropy of a  
-system represented  by a time series are not well suited to  
-analysis  of the short  and noisy  data sets encountered  in 
-biomedical studies [1,18]. Several proposals for calculating  
-entropy used in  this work  are presented.  
-The entropy H(X) of a single discrete random variable  is 
-a measure of its average uncertainty. Shannon entropy [17] is  
-calculated by  the equation:  
- 
-H(X) = − ∑ p(xi) logp (xi) = − E[log p(xi)]          (1) 
-Xi∈Θ 
-Where  X represents  a random  variable  with a set of values  
-             and probability  mass  function  
-p(xi) = P r {X = x i}, xi ∈ Θ, and E represents  the expectation  
-operator.  Note  that p logp  = 0 if p = 0. 
-The Approximate Entropy (ApEn) is a statistical measure  
-that smooth  transient  interference  and can suppress  the 
-influence  of noise  by properly  setting  of the algorithms  
-parameters.  It can be employed  in the analysis  of both 
-stochastic and deterministic signals [19,20]. This is crucial in  
-the case of biological signals, which are outputs of complex  
-biological networks and may be deterministic or stochastic, or  
-both.  ApEn  provides a  model -independent  measure  of the  
-irregularity of the signals. The algorithm summarizes a time  
-series  into a non-negative  number,  with higher  values  
-representing  more  irregular  systems  [19,20].  The method  
-examines time series for similar epochs [21]: more frequent  
-and more  similar  epochs  lead to lower  values  of ApEn.  
-ApEn (m, r, n ) measures  for n points,  sequences  that are 
-similar for m points remain similar, within a tolerance r, at the  
-next point. Thus a low value of ApEn reflects a high degree of  
-regularity. ApEn algorithm counts each sequence as matching  
-itself.  In order  to reduce  this bias, Sample  Entropy  
-SampEn (m, r, n ) was developed,  which  does not count  self- 
-matches.  
-The sample  entropy  statistic  SampEn (m, r, n)is defined  as: 
-SampEn (m, r, n) = lim{−ln(Am(r)/Bm(r))} i=1  
-The scalar  is the tolerance  for accepting  matches.  In the 
-present  investigation,  we used the parameters  recommended  in 
-[22],  with m = 2  and r = 0.2  (standard  deviation  of the 
-sources is normalized to 1). SampEn is a robust quantifier of  
-complexity as for instance EEG signals [23], and can be used  
-to detect  of artifacts  in EEG  recordings  [24].  
-Permutation  entropy directly accounts  for the temporal  
-information contained in the time series; furthermore, it has  
-the quality  of simplicity,  robustness  and very low 
-computational cost [1,3,4]. Bandt and Pompe [25] introduce a  
-simple  and robust  method  based  on the Shannon  entropy  
-measurement  that takes  into account  time causality  by 
-comparing  neighboring  values  in a time series.  The 
-appropriate symbol sequence arises naturally from the time  
-series, with no prior knowledge assumed [1]. The Permutation  
-entropy is calculated for a given time series {x1, x2, … , x n} as a 
-function of the scale factor . In order to be able to compute  
-the     permutation     of      a      new      time      vector      Xj, 
-St = [Xt, X t+1, … , X t+m−1 ] is generated  with the embedding  
-dimension  and then arranged  in an increasing  order:  
-[Xt+j1−1 ≤ X t+j2−1 ≤ ⋯ ≤ X t+jn−1]. Given  different values,  
-there   will   be   m! possible   patterns   , also known  as 
-permutations. If f(π) denotes its frequency in the time series,  
-its relative  frequency  is p(π) = f(π)⁄(L⁄s − m + 1 ). The 
-permutation entropy  is then defined as:  
-m! 
-PE = − ∑ p(πi) ln p(πi) (6) 
-i=1  
-B. Automatic  classification  
-The main goal of the present work is feature search in  
-handwriting aiming at preclinical evaluation in order to define  
-tests for ET diagnosis. These features will define the control  
-group (CR) and the essential tremor group (ET). A secondary  
-goal is the optimization of computational cost with the aim of  
-making  these techniques useful for real -time applications in  
-real environments.  Thus,  automatic  classification  will be 
-modeled  with this in mind.  We used five different  classifiers:  
- A Support Vector Machine (SVM) with polynomial  
-kernel  
- A multi layer perceptron (MLP) with neuron number  
-in hidden  layer  (NNHL)  = 
-max(Attribute/Number+Classes/Number) and training  
-step (TS) = NNHL*10  
- k-NN Algorithm.  
-The WEKA  software  suite  [26] has  been  used to carry  out 
- 
- 
-with  n→∞  
-= −ln(A/B ), (2) the experiments.  The results  were  evaluated  using  
-Classification  Error  Rate (CER,  %). For training  and 
-validation  steps  we used k-fold cross -validation  with k=10.  
-A = [(n − m − 1)(n − m)/2]Am(r), (3) 
-and 
-B = [(n − m − 1)(n − m)/2]Bm(r). (4) 
-Bm(r) is the probability  that  two sequences  match  for 
-   points.  Similarly,  Am(r) is the probability  that  two  
-sequences match  for m + 1 points:  Cross -validation is a robust validation method  for variable  
-selection [27]. Repeated cross -validation (as calculated by the  
-WEKA environment) allows robust statistical tests. We also  
-use the measurement  provided  automatically  by WEKA  
-“Coverage  of cases”  (0.95  level)  that is the confidence  interval  
-at 95% level.  
-K. López de Ipiña  et al., "Selection of entropy based features for the analysis of the Archimedes' spiral applied to 
-essential tremor,"  2015 4th International Work Conference on Bioinspired Intelligence (IWOBI ), 2015, pp. 157 -
-162, doi: 10.1109/IWOBI.2015.7160160 . 
-_________________________________________________________________________________________  
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
- 
-Figure 2. CER (%) for paradigms with linear and non -linear  
-features sets based on entropy: Shannon, ApEn, SmEn and  
-Permutation Entropy.  
-IV. RESULTS  AND  DISCUSSION  
-The experimentation  has been  carried  out with the 
-balanced subset BIODARWO. The goal of these experiments  
-was to examine  the potential  of entropy  algorithms  and 
-selected  features  for automatic  measurement  of the 
-degradation of Archimedes’s spiral drawing with ET. Thus,  
-previously  defined  feature  sets have  been  evaluated  in order  to 
-properly define control and ET groups. In a first stage linear  
-and non-linear  features  have  been  extracted  by several  
-methods described in section III: Shannon entropy, ApEn and  
-SmEn.  Automatic  classification  by described  (section  III) 
-paradigms was performed over the database. The results of  
-CER (%) for paradigms with linear and non -linear features  
-sets are summarized in Figure 2. For both algorithms m=2,3  
-and tolerance  r=0.2.  
- Non Linear Features (NLF) sets increase about 7% 
-the features  number  
- Non-linear  features  sets improve  the results  for all the 
-paradigms  
- Shannon entropy outperforms LF (LFSE) for MLP  
-and k-NN 
- ApEn  improve  system  performance  for m=3 with 
-MLP and SVM.  k-NN has better performance for  
-m=2.  
- SmEn with m=3 appears as the best option for all  
-paradigms.  
- The best option is SmEn with m=3 and MLP with a  
-CER  of 15.69%.  
- 
-In a second  stage  permutation  entropy  is evaluated  for 
-different orders m and time delay. In our particular case and  
-due the signals  are composed  from  5000 -1000  samples,  and  
-parameter was fixed until m=7. The results are also compared  
-with SmEn with m=3 and tolerance of r=0.2. The results are  
-shown  in Figure  3.  PE improves  the results  in most  of the cases  
- The best results  are obtained  with PE and m=7,  t=7. 
- MLP  obtains  the best results  for this last option  
- The best option  is PE-m7t7  with MLP  and CER  of 
-15.65%.  
- Good  results  are achieved  even  with k-NN with less 
-computational  cost. 
-V. CONCLUSIONS  
-This work,  on selection  of nonlinear  biomarkers  from  
-drawings  and handwriting, is part of a wide -ranging cross  
-study for the diagnosis of essential tremor which is developed  
-in Biodonostia Health Institute. Specifically the main goal of  
-the present work is the analysis of features in Archimed es’s 
-spiral drawing, one of the most used standard tests for clinical  
-diagnosis of ET.  In this sense entropy based features have  
-been  add to a set of classical  linear  features  (static  and 
-dynamics).  Several  entropy  algorithms  have  been  evaluated  by 
-an automatic  analysis  system  consists  of several  Machine  
-Learning. The best option is MLP with permutation entropy  
-and good results are obtained even with k-NN. Then these  
-new biomarkers will be integrated in future works with those  
-obtained in the Biodonosti a study.  It should be highlighted  
-that the use of this technology  could  provide  undoubted  
-benefits towards the development of more sustainable, low  
-cost, high quality, non -invasive technologies. These systems  
-are easily adaptable to the user and environment, and from a  
-social and economic point of view can be very useful in real  
-complex  environments.  In future  works  new non-linear  
-features,  entropy  algorithms  and automatic  selection  
-methodologies  will be used.  
- 
- 
-Figure 3. CER (%) for the paradigms with the references  
-Linear Features (LF) and Shannon Entropy (SE) and other  
-entropy  paradigms  
-K. López de Ipiña  et al., "Selection of entropy based features for the analysis of the Archimedes' spiral applied to 
-essential tremor,"  2015 4th International Work Conference on Bioinspired Intelligence (IWOBI ), 2015, pp. 157 -
-162, doi: 10.1109/IWOBI.2015.7160160 . 
-_________________________________________________________________________________________  
-Acknowledgments  
-This work has been partially supported by the University  
-of the Basque  Country  by UPV/EHU —58/14  project,  
-SAIOTEK from the Basque Government, University of Vic - 
-Central  University  of Catalonia  under  the research  grant  
-R0904, and the Spanish Ministerio de Ciencia e Innovación  
-TEC2012 -38630 -C04-03. 
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txt/2203.10517.txt

@@ -1,1153 +0,0 @@
-IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020 1
-Learning Whole Heart Mesh Generation From
-Patient Images For Computational Simulations
-Fanwei Kong, Shawn C. Shadden
-Abstract — Patient-specific cardiac modeling combines
-geometries of the heart derived from medical images
-and biophysical simulations to predict various aspects of
-cardiac function. However, generating simulation-suitable
-models of the heart from patient image data often requires
-complicated procedures and significant human effort. We
-present a fast and automated deep-learning method to
-construct simulation-suitable models of the heart from
-medical images. The approach constructs meshes from
-3D patient images by learning to deform a small set of
-deformation handles on a whole heart template. For both
-3D CT and MR data, this method achieves promising ac-
-curacy for whole heart reconstruction, consistently outper-
-forming prior methods in constructing simulation-suitable
-meshes of the heart. When evaluated on time-series CT
-data, this method produced more anatomically and tempo-
-rally consistent geometries than prior methods, and was
-able to produce geometries that better satisfy modeling
-requirements for cardiac flow simulations. Our source code
-and pretrained networks are available at https://github.
-com/fkong7/HeartDeformNets .
-Index Terms — Geometric Deep Learning, Mesh Genera-
-tion, Shape Deformation, Cardiac Simulations
-I. INTRODUCTION
-IMAGE-based cardiac modeling is used to simulate various
-aspects of cardiac function, including electrophysiology
-[1], hemodynamics [2] and tissue mechanics [3]. This method
-derives geometries of the heart from patient image data and
-numerically solves mathematical equations that describe var-
-ious physiology on discretized computational domains. Such
-“digital twin” modeling of a patient’s heart can provide infor-
-mation that cannot be readily measured to facilitate diagnosis
-and treatment planning [4]–[6], or to quantify biomechanical
-underpinnings of diseases [7]. This paradigm has motivated
-numerous research efforts on a wide range of clinical applica-
-tions, such as, simulations of the stress and strain of cardiac
-tissues when interacting with implantable cardiac devices [8],
-the cardiac flow pattern after surgical corrections [4], [6], and
-cardiac rhythm outcome after ablation surgery [5].
-This work was supported by the NSF , Award No. 1663747. We
-thank Drs. Shone Almeida, Amirhossein Arzani and Kashif Shaikh for
-providing the time-series CT image data.
-Fanwei Kong, is with the Department of Mechanical Engineering,
-University of California at Berkeley, Berkeley, CA 94720 USA (e-mail:
-fanwei [email protected]).
-Shawn C. Shadden, is with the Department of Mechanical Engineer-
-ing, University of California at Berkeley, Berkeley, CA 94720 USA (e-
-mail: [email protected]).Generating simulation-suitable models of the heart from
-image data has remained a time-consuming and labor-intensive
-process. It is the major bottleneck limiting large-cohort val-
-idations and clinical translations of functional computational
-heart modeling [2], [9]. Indeed, prior studies have been limited
-to only a few subjects [2], [4], [5]. The entwined nature of the
-heart makes it difficult to differentiate individual cardiac struc-
-tures, and typically a complicated series of steps are needed
-to identify and label various structures for the assignment
-of boundary conditions or modeling parameters. Deforming-
-domain computational fluid dynamics (CFD) simulations of
-the intracardiac hemodynamics, is particularly labor-intensive
-since it requires reconstructing temporally-consistent deforma-
-tions of the heart from a sequence of image snapshots.
-Deep learning methods can train neural networks from
-existing data to automatically process medical images and gen-
-erate whole heart reconstructions. Most deep learning methods
-have, however, focused on segmentation (pixel classification)
-rather than construction of a computer model of the heart,
-usually represented by tessellated meshes [10]. Prior studies
-on automated cardiac mesh reconstruction thus adopted multi-
-stage approaches, where segmentation of the heart was first
-generated by convolutional neural networks (CNN) and surface
-meshes of the heart were then created from the marching cube
-algorithms and following surface post processing methods
-[11]. However, the intermediate segmentation steps often intro-
-duce extraneous regions containing topological anomalies that
-are unphysical and unintelligible for simulation-based analyses
-[11]. Direct mesh reconstruction using geometric deep learning
-[12], [13] provides a recent avenue to address the end-to-end
-learning between volumetric medical images and simulation-
-ready surface meshes of the heart [14]–[16]. However, these
-approaches often assumes the connectivity of the meshes.
-That is, the shape and topology of the predicted meshes from
-these approaches are pre-determined by the mesh template
-and cannot be easily changed to accommodate various mesh
-requirements for different cardiac simulations.
-To overcome these short comings, we propose to learn to
-deform the space enclosing a whole heart template mesh to
-automatically and directly generate meshes that are suitable
-for computational simulations of cardiac function. Here we
-propose to leverage a control-handle-based shape deformation
-method to parameterize smooth deformation of the template
-with the displacements of a small set of control handles and
-their biharmonic coordinates. Our approach learns to predict
-the control handle displacements to fit the whole heart templatearXiv:2203.10517v2  [eess.IV]  8 Nov 20232 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-to the target image data. We also introduce learning biases to
-produce meshes that better satisfy the modeling requirements
-for computational simulation of cardiac flow. The contributions
-of this work are summarized as follows:
-1) We propose a novel end-to-end learning method combin-
-ing deformation handles to predict deformation of whole
-heart mesh templates from volumetric patient image
-data. We show that our approach achieves comparable
-geometric accuracy for whole heart segmentation as
-prior state-of-the-art segmentation methods.
-2) We introduced novel mesh regularization losses on
-vessel inlet and outlet structures to better satisfy the
-meshing requirements for CFD simulations. Namely, our
-method predicts meshes with coplanar vessel caps that
-are orthogonal to vessel walls for CFD simulations.
-3) We validated our method on creating 4D dynamic whole
-heart and left ventricle meshes for CFD simulation
-of cardiac flow. Our method can efficiently generate
-simulation-ready meshes with minimal post-processing
-to facilitate large-cohort computational simulations of
-cardiac function.
-A. Learning-Based Shape Deformation
-Shape deformation using low-dimensional control of de-
-formation fields has been extensively studied for decades in
-computer graphics and has been ubiquitously used in anima-
-tion. These methods usually interpolate the transformation of a
-sparse set of control points to all points on the shape. Among
-the most popular approaches were are free-form-deformation
-that uses a regular control point lattice to deform the shape
-enclosed within the lattice [17], cage-deformation that uses
-a convex control cage that encloses the shape [18], as well
-as control-handle-based approaches that directly place control
-points on the surface of the shape [19]–[21]. Recent works
-have shown success in integrating these shape deformation
-methods in deep-learning frameworks for automated mesh
-reconstruction from single-view camera images [22], gener-
-ative shape modeling [23] as well as deformation transfer
-[24]. However, these approaches were designed to take 2D
-camera images or 3D meshes as input and used memory-
-intensive CNNs or fully connected neural network to predict
-the transformation of control points. They thus cannot be
-directly applied to deform complicated whole heart structures
-from high-resolution 3D medical image data. Therefore, we
-herein propose to use graph convolutional networks (GCN)
-and sparsely sampling of the volumetric image feature map to
-predict control point translations and thus efficiently produce
-meshes from 3D medical images.
-B. Mesh Reconstruction From 3D Medical Images
-Recent works on direct mesh reconstruction from volumet-
-ric images aim to deform an initial mesh with pre-defined
-topology to a target mesh [14], [15]. Our previous approach
-leveraged a GCN to predict deformation on mesh vertices
-from a pre-defined mesh template to fit multiple anatomical
-structures in a 3D image [15]. However, different structures
-were represented by decoupled mesh templates and thus stillrequired post-processing to merge different structures for com-
-putational simulations involving multiple cardiac structures.
-Similarly, [16] used deep neural networks and patient metadata
-to predict cardiac shape parameters of a pre-built statistical
-shape model of the heart. Our approach presented herein, in
-contrast, deforms the space enclosing the mesh template. Once
-being trained on the whole heart template, our network can
-deform alternative template meshes that represent a subset
-of the geometries in the template to accommodate different
-modeling requirements.
-A few studies have focused on learning space deformation
-fields. [25] used a 3D UNet to predict a deformation field to
-deform heart valve templates from CT images. Additionally,
-our preliminary work combined free-form deformation (FFD)
-with deep learning to predict the displacement of a control
-point grid to deform the space enclosing a simulation-ready
-whole heart template [26]. However, predicting the defor-
-mation fields requires many degrees of freedom to produce
-accurate results. For example, since FFD has limited capability
-for complex shape deformation, our prior method required a
-dense grid of thousands of control point to achieve acceptable
-whole heart reconstruction accuracy. Herein we demonstrate
-that using control-handle-based deformation with biharmonic
-coordinates achieves higher reconstruction accuracy while
-using far less control points than the FFD-based approach.
-II. M ETHODS
-A. Shape Deformation Using Biharmonic Coordinates
-We parameterize deformations of whole heart meshes with
-the translations of a small set of deformation handles sampled
-from the mesh template. Given a set of mesh vertices V∈
-Rn×3and a set of control points P∈Rc×3, we compute
-the biharmonic coordinates W∈Rn×c, which is a linear
-map, V=WP.nandcare the number of vertices and the
-number of control points, respectively. Wis defined based on
-biharmonic functions and can be pre-computed by minimizing
-a quadratic deformation energy function while satisfying the
-handle constraints with linear precision [21]. Namely, let
-Q∈Rc×nbe the binary selector matrix that selects rows of X
-corresponding to the control handles, and let T∈R(n−c)×n
-be the complementary selector matrix of Qcorresponding to
-the free vertices. W is computed by
-V= arg min
-X∈Rn×31
-2trace(XTAX),subject to QX=P (1)
-V= (QT−TT(TATT)−1TAQT)| {z }
-WP (2)
-where Ais a positive semi-definite quadratic form based on
-the squared Laplacian energy to encourage smoothness [21].
-Under this framework, displacements of the control handles
-can smoothly deform the underlying mesh template.
-B. Network Architecture
-Figure 1 shows the overall architecture of our network. The
-central architecture is the novel control-handle-based mesh de-
-formation module, which learns to predict the displacements ofKONG et al. : LEARNING WHOLE HEART MESH GENERATION FROM PATIENT IMAGES FOR COMPUTATIONAL SIMULATIONS 3
-Fig. 1. Diagram of the proposed automatic whole heart reconstruction approach. A total of three deformation blocks were used to progressively
-deform the mesh templates, using 75, 75 and 600 control handles, respectively, for the 3 deformation blocks.
-control handles based on image features, so that the underlying
-mesh templates can be smoothly deformed to match the input
-3D image data.
-1) Image Encoding and Segmentation Modules: We applied
-a residual 3D CNN backbone to extract and encode image
-features at multiple resolutions [27]. The CNN backbone
-involves 4 down-sampling operations so that image feature
-volumes at 5 different resolution are obtained. These image
-feature volumes are used as inputs to GCN layers to predict
-the displacements of control handles. Similar to [15], we also
-used a segmentation module that predicted a binary segmen-
-tation map to enable additional supervision using ground truth
-annotations. This module was only used during training.
-2) Mesh Deformation Module: Biharmonic coordinates con-
-strain the displaced control handles to be located on the
-deformed mesh template. Therefore, regardless of which set
-of control handles are sampled, these handles will be located
-at the corresponding positions on the template mesh. We
-used a neural network to update the coordinates of all points
-(S∈Rn×3) on the mesh template and obtain the coordinates
-of the selected control handles ( P∈Rc×3) from the updated
-mesh vertex locations to deform the template using the pre-
-computed biharmonic coordinates. This design allows picking
-arbitrary sets of control handles to deform the template at
-various resolutions after training. Furthermore, training to pre-
-dict the coordinates of every mesh vertex provides additional
-supervision that can speed up training.
-Since the mesh template can be represented as a graph,
-a GCN was used to predict the mesh vertex displacements.
-We chose to approximate the graph convolutional kernel with
-a first order Chebyshev polynomial of the normalized graph
-Laplacian matrix [12]. At each mesh vertex, we extracted the
-image feature vectors at the corresponding image coordinatesfrom multiple image feature volumes with various resolution.
-These image feature vectors were then concatenated with the
-mesh feature vectors following a GCN layer. The combined
-vertex feature vectors were then processed by three graph
-residual blocks. We then used an additional GCN layer to
-predict displacements as 3D feature vectors on mesh vertices.
-We used a total of three deformation blocks to progressively
-deform the template mesh. The first and second deformation
-blocks used 75 control handles to deform the mesh, whereas
-the last deformation block used more control handles, 600, for
-a more detailed prediction.
-C. Network Training
-Fig. 2. Graphical illustration of different loss functions. Y ellow and teal
-on the right panel shows the caps and walls to apply the mesh regular-
-ization losses, respectively, and arrows shows cap normal vectors.4 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-The training of the networks was supervised by 3D ground
-truth meshes of the whole heart as well as a binary segmenta-
-tion indicating occupancy of the heart on the input image grid.
-We used the following loss functions (illustrated in figure 2)
-in training to produce accurate whole heart geometries while
-ensuring the resulting mesh is suitable to support simulations.
-1) Geometric Consistency Losses: The geometric consis-
-tency loss Lgeois the geometric mean between the point
-and normal consistency losses to supervise the geometric
-consistency between the prediction and the ground truth [15].
-We note that edge length and Laplacian regularization losses,
-such as used in [15], are not necessary since the smoothness of
-the mesh prediction is naturally constrained by the biharmonic
-coordinates used to deform the template. Since only the
-selected control points were used to deform the mesh template
-while the displacements of all mesh points were predicted, we
-needed to regularize the L2distances between the mapped
-mesh points ( S∈Rn×3) and the corresponding mesh vertices
-on the deformed mesh template ( V∈Rn×3). This consistency
-loss between the points and the mesh ensures that coordinates
-of other unselected control points also result in reasonable
-deformations. In other words, the deformation results should
-not be sensitive to the choice of pre-selected control points.
-2) Mesh Regularization for CFD Simulations: Cardiac mod-
-els generally includes portions of the great vessels connected
-to the heart (e.g., pulmonary veins and arteries, venae cavae,
-and aorta). For CFD simulation of cardiac flow, locations
-where these vessels are truncated (so-called inflow and out-
-flow boundaries, or “caps”) should be planar and nominally
-orthogonal to the vessel. On our training template, we labeled
-these caps, as well as the associated vessel walls. Figure 2
-shows the identified cap and wall faces on left atrium (LA),
-right atrium (RA) and aorta. We applied a co-planar loss
-on each cap that penalizes the L2differences of the surface
-normals on the cap. Namely, Lcoplanar =P
-kP
-j∈Ck||nj−
-1
-|Ck|P
-j∈Cknj||2
-2where Ckis the set of mesh faces for the
-kth cap and njis the normal vector for the jth face on Ck.
-For mesh vertices that are on the vessel walls near the caps,
-we minimized the absolute value of the dot product between
-the surface normal vectors and the surface normal vector of
-the caps to encourage orthogonality. Namely, Lorthogonal =P
-kP
-j∈Wk|⟨nk,1
-|Ck|P
-p∈Cknp⟩|,where Wkis the set of
-mesh faces on the vessel wall that corresponds to the kth cap.
-3) Weighted Mesh Losses: Patient images may not always
-contain the targeted cardiac structures. As shown in Figure
-2 (left), cardiac structures such as pulmonary veins, pul-
-monary arteries and the aorta are often not captured in full,
-although the truncks of these structures can be necessary for
-simulations. We thus aim to predict “complete” whole heart
-structures from incomplete image data. Namely, we computed
-the bounding box of the ground truth meshes and assigned zero
-weights within the geometric consistency loss for predicted
-mesh vertices that were located outside the bounding box.
-Furthermore, as the geometry of inlet vessels are important
-to the accuracy of CFD results, we applied a higher weight
-for the geometric consistency loss on mesh vertices that are
-located on vessel walls near the inlets.4) Total Losses: The total loss on a predicted mesh Mis
-Lmesh(M,G, W ) =X
-iLgeo(Mi, Gi,wi)
-+αX
-k(Lcoplanar (M) +βLorthogonal (M))(3)
-where Girepresents the ground truth mesh for individual
-cardiac structure, and wirepresents the weighting vector for
-each mesh point. Mcan be both the deformed whole heart
-mesh template Vand the mesh obtained from mapping all
-mesh points S. The total loss for training is a weighted sum
-of the mesh losses and the segmentation loss, which is the
-sum of the binary cross-entropy and the dice losses between
-the predicted occupancy probability map Ipand the ground
-truth binary segmentation of the whole heart Ig.
-Ltotal=λ1Lmesh(S, G, W ) +λ2Lmesh(V, G, W )+
-λ3||S−V||2
-F+Lseg(Ig, Ip)(4)
-5) Image Augmentation for Shape Completion: Leveraging
-the mesh template, we trained our method to predict the
-geometries of the whole heart represented by the template
-mesh when the images do not cover the complete cardiac
-structures. Since CT images often do not cover the whole
-heart, we selected CT images that did cover the whole heart
-(n=10) from our training set and then generated 10 random
-crops for each image while keeping the ground truth meshes
-to be the same. Figure 3 visualizes example image crops
-and the corresponding ground truth meshes. We also applied
-Fig. 3. Visualization of augmented input image crops and the corre-
-sponding ground truth meshes.
-random shearing, rotations and elastic deformations, following
-the same augmentation strategies described in our prior work
-[15].
-III. E XPERIMENTS
-A. Datasets and Experiments
-1) Task-1: Whole Heart Segmentation for 3D Images: We
-applied our method to public datasets of contrast-enhanced
-3D CT images and 3D MR images from both normal and
-abnormal hearts. For training and validation, we used a total
-of 102 CT images and 47 MR images from the multi-
-modality whole heart segmentation challenge (MMWHS) [10],
-orCalScore challenge [28], left atrial wall thickness challenge
-[29] and left atrial segmentation challenge [30]. Among them,
-we used 87 CT and 41 MR images for training, and we used 15
-CT images and 6 MR images for validation, where we tuned
-the hyper-parameters and selected the model trained with the
-hyper-parameter set that performed best on the validation data.
-We then evaluated the final performance of the selected modelKONG et al. : LEARNING WHOLE HEART MESH GENERATION FROM PATIENT IMAGES FOR COMPUTATIONAL SIMULATIONS 5
-on a held out test dataset from the MMWHS challenge, which
-contained 40 CT and 40 MR images. For CT images, the
-in-plane resolutions vary from 0.4×0.4mm to 0.78×0.78
-mm and the through-plane resolutions vary from 0.5mm to
-1.6mm. For MR images, the in-plane resolutions vary from
-1.25×1.25mm to 2×2.mm and the through-plane resolutions
-vary from 2.mm to 2.3mm.
-Fig. 4. Illustration of example CT and MR image data, the correspond-
-ing surface meshes generated from manual segmentation using the
-marching cube algorithm, and the resulting ground truth surface meshes
-after post processing.
-For each image in the dataset, we followed the MMWHS
-challenge [10] and created ground truth segmentation of 7
-cardiac structures to supervise the training and evaluate model
-performance on validation and test datasets. The 7 cardiac
-structures included the blood cavities of left ventricle (LV),
-right ventricle (RV), left atrium (LA), right atrium (RA),
-LV myocardium (Myo), aorta (Ao), and pulmonary artery
-(PA), for all images. Figure 4 illustrates our pipeline to
-generate smooth ground truth surface meshes from the manual
-segmentations. We resampled the segmentation to a resolution
-of1.×1.×1.mm, then used the marching cube algorithm
-to generate the surface meshes for each cardiac structure. We
-then applied a Windowed-Sinc smoothing filter [31] with a low
-pass band of 0.01 and 20 iterations of smoothing to generate
-smooth ground truth meshes. Furthermore, as visualized in the
-example CT case in Figure 4, surface meshes were clipped at
-the image bounding box to remove the fictitious surface at
-the image boundaries for cardiac structures that exceeded the
-coverage of the image data.
-We compared the geometric accuracy of the reconstructed
-whole heart surfaces against prior deep-learning methods that
-demonstrated strong performance of segmenting whole heart
-geometries from 3D medical images. Namely, we considered
-HeartFFDNet [26], our prior work that generates simulation-
-ready whole heart surface meshes from images by learning
-free-form deformation from a template mesh, MeshDeformNet
-[15] that predicts displacements on sphere mesh templates, as
-well as 2D UNet [32] and a residual 3D UNet [27] that are
-arguably the most successful neural network architecture for
-image segmentation. We also implemented a SpatialConfigu-
-ration Net (SCN) [33] that ranked first for CT and second for
-MRI in the MMWHS challenge, using our residual 3D UNet
-backbone. This segmentation-based approach incorporates rel-
-ative positions among structures to focus on anatomically
-feasible regions. All methods were trained on the same trainingand validation data splits, and used the same pre-processing
-and augmentation procedures to ensure a fair comparison.
-2) Task-2: Whole Heart Mesh Construction for 4D Images:
-We applied our method on time-series CT images to evaluate
-its performance on creating whole heart meshes for CFD
-simulations. Since the MMWHS dataset does not include
-pulmonary veins, LA appendage or venae cavae, we prepared
-another set of ground truth segmentations to include these
-structures. The geometric accuracy and the mesh quality of
-the reconstructed meshes for CFD simulations were then
-evaluated on 10 sets of time-series CT images against the
-learning-based mesh reconstruction baselines, HeartFFDNet
-and MeshDeformNet.
-Fig. 5. Visualization of simulation-ready templates with trimmed
-inlet/outlet geometries and tagged face IDs for prescribing boundary
-conditions.
-3) Task-3: CFD Simulations: We conducted CFD simula-
-tions of cardiac flow using the predicted whole heart meshes
-from time-series CT images. Since our predicted model does
-not contain heart valves, only diastolic flow was simulated.
-We also conducted CFD simulations for the LV and simulated
-the LV flow for the entire cardiac cycle. Figure 5 visualizes
-the simulation-ready templates of the 4 heart cambers and
-the LV with trimmed inlet/outlet geometries and tagged face
-IDs for prescribing boundary conditions. These simulation-
-ready templates were manually created from the training whole
-heart template in a surface processing software, SimVascular
-[34].We linearly interpolated the pre-computed biharmonic
-coordinates onto the new templates so that our trained mod-
-els can readily deform these new templates. The simulation
-results were compared against results obtained from time-
-series ground truth meshes created manually in SimVascular
-[34]. We also compared simulation results among our method,
-HeartFFDNet, and a conventional semi-automatic model con-
-struction pipeline based on image registration, where a manu-
-ally created ground truth mesh was morphed based on trans-
-formations obtained from registering images across different
-time points.
-B. Evaluation Metrics
-We used Dice similarity coefficient (DSC) and Hausdorff
-Distance (HD) to measure segmentation accuracy. The DSC
-and HD values for the MMWHS test dataset were evalu-
-ated with an executable provided by MMWHS organizers.
-For mesh-based methods, we converted the predicted surface
-meshes to segmentation prior to evaluation. Mesh quality was
-compared in terms of the percentage self-intersection, which
-measures the local topological correctness of the meshes,6 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-orthogonality of the vessel caps with respect to the vessel
-walls, as well as the coplanarity of the vessel caps. The
-percentage mesh self-intersection was calculated as the per-
-centage of intersected mesh facets detected by TetGen [35]
-among all mesh facets. The orthogonality between vessel
-caps and walls (Cap-Wall-Orthogonality) was measured by
-the normal consistency between the mean cap normal vector
-and the vector connecting the centroids of the mesh points
-on the cap and on the wall, respectively. Namely, CWO =P
-k1− ⟨1
-|Wk|P
-i∈Wk|ni,1
-|Ck|P
-j∈Ck|nj⟩where WkandCk
-represent the sets containing the mesh vertices on a vessel wall
-and the corresponding vessel cap. Vessel caps coplanarity was
-measured by the projected distance between the mesh vertices
-on the cap and the best fit plane over those mesh vertices. For
-CFD simulations, we compared integrative measures during a
-cardiac cycle, namely, LV volume and average kinetic energy
-KE′=1
-2VLVR R R
-ρu2dV, where VLVis the volume of the
-LV and uis the flow velocity. We also compared the mean
-velocity near the mitral valve opening (MO) and aortic valve
-opening (AO) during a cardiac cycle. Paired t-test was used
-for statistical significance.
-C. Deforming-Domain CFD simulations of Cardiac Flow
-We applied the Arbitrary Lagrangian-Eulerian (ALE) for-
-mulation of the incompressible Navier-Stokes equations to
-simulate the intraventricular flow and account for deforming
-volumetric mesh using the finite element method. Since time
-resolution of image data is too coarse (about 0.1s) to be used
-directly in time-stepping of the Navier–Stokes equations, cubic
-spline interpolation was applied to interpolate the meshes
-predicted at different imaging time points so that the time
-resolution of the interpolated meshes was 0.001s, which cor-
-responded to the simulation time step. For the fluid domain,
-the mesh motions computed from these interpolated meshes
-were imposed as Dirichlet boundary conditions on the chamber
-walls. For simulations of LV flow, we imposed Dirichlet
-boundary conditions on the mitral inlet during systole, and
-on the aortic outlet during diastole. Neumann (prescribed
-pressure) boundary conditions were applied to the mitral inlet
-during diastole or to the aortic inlet during systole. Diastole
-and systole phases were determined based on the increase and
-decrease of the LV volume. For simulations of diastolic cardiac
-flow within 4 heart chambers, we applied Neumann boundary
-conditions to the pulmonary vein inlets, and imposed Dirichlet
-boudary condition on the aortic outlet. Blood was assumed to
-have a viscosity µof4.0×10−3Pa·sand a density ρof
-1.06g/cm3. The volumetric mesh was created automatically
-from our predicted surface mesh using TetGen [35], using a
-maximum edge size of 1.5mm. The equations were solved with
-the open-source svFSI solver from the SimVascular project
-[36].
-IV. R ESULTS
-A. Comparative Studies of Whole Heart Segmentation
-Performance on MMWHS Dataset
-1) Comparison of Geometric Accuracy with Other Methods:
-Table I compares the average Dice scores and Hausdorffdistances of the reconstruction results of both the whole heart
-and the individual cardiac structures for the MMWHS test
-dataset. We show the accuracy of deforming the template by
-mapping all mesh points ( S) and by interpolating the mesh
-deformation using only 600 uniformly-sampled control han-
-dles ( V). Mapping all points consistently achieved higher dice
-scores than using 600 selected control handles, but the HDs are
-worse for some cardiac structures. For both CT and MR data,
-in terms of Dice scores, our method consistently outperformed
-HeartFFDNet and 3D UNet for all cardiac structures and
-achieved comparable performance with MeshDeformNet, 2D
-UNet, 3D SCNet for most cardiac structures. Our method
-achieved the best HDs for LA, RA and RV for CT data and
-for all cardiac structures except for aorta and PA for MR data.
-Figure 6 presents the best, median, and worst segmentation
-results of our method on CT and MRI test images and pro-
-vides qualitative comparisons of the results from the different
-methods. As shown, mesh-based approaches, ours, HeartFFD-
-Net and MeshDeformNet produced smooth and anatomically
-consistent cardiac geometries while segmentation-based ap-
-proaches, 2D UNet, residual 3D UNet, 3D SCNet produced
-segmentations with topological artifacts such as missing parts,
-holes, and isolated islands. Although 3D SCNet produced
-higher Dice scores than residual 3D UNet, it produced a
-few misclassifications where the LA was incorrectly classified
-into RV . Although MeshDeformNet produced smooth and
-anatomically consistent cardiac geometries, it was prone to
-gaps between adjacent cardiac structures by deforming un-
-coupled spheres. Our method and the HeartFFDNet were able
-to avoid this limitation by deforming the space enclosing
-a whole heart template, preserving the connections among
-cardiac structures.
-2) Effect of Varying Control Handle Numbers: We investi-
-gated the effect of various design choices on the whole heart
-segmentation performance of our proposed method. Table II,
-presents the effect of varying the number of control handles
-used during training on the average Dice scores and Hausdorff
-distances of the reconstruction results. Increasing the number
-of control handles used in the last deformation block from
-75 to 900 generally resulted in increased performance for
-most cardiac structures. However, the resulting improvement
-in terms of Dice scores was only around 1%, indicating
-the robustness of our method towards using relatively fewer
-numbers of control handles. Similarly, using more control
-handles in the first and/or second deformation blocks did not
-result in significant improvement for most cardiac structures.
-Therefore, in our final network model, we chose to use a small
-number of control handles (75) in the first and second blocks
-to reduce the computational cost, and used 600 control handles
-in the last deformation block for a slightly better performance.
-3) Effect of Individual Loss Components on Whole Heart
-Segmentation Performance: Since our training pipeline in-
-volves a joint supervision of multiple objectives, we performed
-an ablation study on the total training loss Ltotal to evaluate
-the contribution of individual loss components. Namely, we
-trained network models while removing the segmentation loss
-Lseg, and the L2consistency loss ||S−V||2
-Fto investigate
-the effectiveness of supervising a segmentation branch, andKONG et al. : LEARNING WHOLE HEART MESH GENERATION FROM PATIENT IMAGES FOR COMPUTATIONAL SIMULATIONS 7
-TABLE I
-COMPARISON OF WHOLE -HEART SEGMENTATION PERFORMANCE , DSC ( ↑)AND HD ( MM) (↓),FROM DIFFERENT METHODS ON THE MMWHS
-CT AND MR TEST DATASETS .* D ENOTES SIGNIFICANT DIFFERENCE OF"OURS (S)" F ROM THE OTHERS (P-VALUES <0.05)
-CT MR
-Method Myo LA LV RA RV Ao PA WH Myo LA LV RA RV Ao PA WH
-DCSOurs (S) 90.07 93.18 93.47 89.48 91.48 93.33 85.60 91.76 80.45 86.98 91.61 88.08 88.09 85.76 78.14 87.41
-Ours (V) 88.38* 92.53* 91.99* 88.76* 90.59* 91.25* 84.73* 90.53* 78.62* 86.27* 89.38* 87.79* 87.20* 83.30* 77.55 86.04*
-HeartFFDNet 83.85* 90.55* 89.38* 86.33* 87.65* 90.65* 80.20* 87.82* 70.67* 83.27* 86.92* 84.47* 82.77* 79.71* 69.68* 81.33*
-MeshDeformNet 89.94 93.23 93.98 * 89.18 91.00 94.98 * 85.22 91.80 79.71 88.13 92.23 88.82 89.24 *88.98 *81.65 *88.17 *
-2DUNet 89.87 93.08 93.06 87.71* 90.49* 93.43 83.23* 91.09* 79.47 86.41 89.61* 85.21 86.48* 86.94 77.24 85.94*
-3DUNet 86.34* 90.17* 92.28* 86.77* 87.58* 92.34* 81.29* 88.78* 76.11* 85.20* 87.90* 86.63* 82.77* 74.18* 76.38 84.04*
-3DUNet+SCN 90.09 92.63* 93.43 89.01 90.49* 91.61 84.05 91.18* 78.36* 85.77* 89.84* 88.09 87.19 84.35 76.85 86.12*
-HDOurs (S) 14.41 10.72 10.41 13.80 11.63 6.59 7.88 16.95 16.39 12.12 11.93 13.93 14.76 7.19 9.12 19.97
-Ours (V) 14.40 8.18* 6.87* 12.46 *9.55* 5.54* 8.45 16.63* 15.96 *10.16 *8.97* 12.56 *12.46 * 7.39 9.14 18.91 *
-HeartFFDNet 14.20 8.74* 7.66* 13.24 10.44* 6.17 8.35 16.57 18.21* 12.43 12.57 15.57 16.36 9.26* 11.36* 22.28*
-MeshDeformNet 14.39 10.41 10.33 13.67 13.36 5.27* 9.16* 17.62 16.92 12.22 11.63 15.05 14.73 6.05* 7.79* 21.08
-2DUNet 9.98* 9.34 6.10* 13.78 10.39 5.63 8.38 16.37 20.34 10.78* 10.62 17.43 16.32 6.98 8.09 27.54*
-3DUNet 13.64 11.47 10.12 16.56* 16.17* 6.88 9.88* 19.91* 34.97* 33.81* 36.28* 24.36* 27.72* 15.52* 10.13 51.54*
-3DUNet+SCN 13.78 12.80 9.86 17.02 17.70* 6.57 7.77 28.04* 39.30* 34.87* 21.06* 28.37* 28.20* 8.05 8.49 53.76*
-Fig. 6. Example segmentation results for CT and MR images from different methods. The CT or MR images that our method had the best,
-the median, and the worst Dice scores among the CT or MR test data were selected, thus illustrating best, typical, and worst segmentation
-results, respectively. The gold arrows indicate locations of artifacts unsuitable for simulations, such as gaps between adjacent cardiac structures for
-MeshDeformNet, missing structures, holes, noisy boundaries, and isolated islands for the segmentation-based methods.
-the effectiveness of encouraging the consistency between
-directly mapped mesh points ( S) and deformed mesh vertex
-locations ( V), respectively. We trained two additional models
-while removing supervision on Vor on S, respectively, to
-validate the effectiveness of supervising both meshes together.
-Table III presents the effect of removing individual loss
-components on the whole heart segmentation performance
-evaluated on the MMWHS test dataset. Removing any of theaforementioned objectives resulted in a significant decrease
-in whole heart Dice scores for both CT and MR data, as
-well as decreased Dice scores for most cardiac structures. The
-Hausdorff distances increased significantly for most cardiac
-structures when supervision on the smoothly deformed mesh
-template Vwas removed. However, there were no significant
-changes in Hausdorff distances for most cardiac structures
-following the removal of other objectives.8 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-TABLE II
-ACOMPARISON OF WHOLE -HEART SEGMENTATION PERFORMANCE , DSC ( ↑)AND HD ( MM) (↓)ON THE MMWHS CT AND MR TEST DATASETS ,
-WHEN USING DIFFERENT NUMBERS OF CONTROL HANDLES DURING TRAINING . B1/B2/B3 D ENOTES THE NUMBERS OF CONTROL HANDLES
-USED IN THE THREE DEFORMATION BLOCKS . * D ENOTES SIGNIFICANT DIFFERENCE OFOURFINAL NETWORK MODEL USING "75/75/600"
-CONTROL HANDLES FROM THE OTHERS (P-VALUES <0.05)
-CT MR
-B1/B2/B3 Myo LA LV RA RV Ao PA WH Myo LA LV RA RV Ao PA WH
-DCS75/75/600 90.07 93.18 93.47 89.48 91.48 93.33 85.60 91.76 80.45 86.98 91.61 88.08 88.09 85.76 78.14 87.41
-75/75/75 88.08* 92.48* 92.54* 88.42* 90.98* 92.76 84.77 90.77* 79.51 86.57 90.71* 87.42 87.55 83.21* 74.40* 86.33*
-75/75/150 88.37* 92.59* 92.37* 88.26* 90.65* 92.78 84.20* 90.69* 78.40* 85.84* 90.64* 86.99* 86.84* 82.81* 74.67* 85.89*
-75/75/300 88.31* 93.47 93.28 89.20 91.28 93.96 84.84 91.34* 80.23 87.98 92.39 * 87.08* 87.94 86.84 78.98 87.46
-75/75/900 89.04* 93.20 93.36 89.54 91.64 93.97 *85.99 91.66 78.64* 87.07 91.54 86.91* 86.77 84.73 75.22 86.48*
-75/300/600 89.91 93.22 93.27 89.17 90.73* 93.83 85.48 91.52 79.74 86.00* 91.18 88.26 87.61 83.94 73.17* 86.66*
-600/600/600 89.53 93.01 93.07 88.43* 90.87* 93.32 84.95 91.25* 79.87 86.32 91.33 87.20* 87.34 83.42* 73.59* 86.55*
-HD75/75/600 14.41 10.72 10.41 13.80 11.63 6.59 7.88 16.95 16.39 12.12 11.93 13.93 14.76 7.19 9.12 19.97
-75/75/75 14.33 10.89 11.18 14.14 11.21 6.87 7.88 16.76 16.24 12.19 12.43 14.62 14.18 8.22 9.90 20.00
-75/75/150 14.33 9.98 9.75 15.38* 12.33* 5.86* 9.40* 17.59 16.54 12.33 11.89 15.27* 15.31 8.48* 10.86* 20.53
-75/75/300 14.29 10.88 10.67 14.70 11.64 6.35 7.80 17.74 16.95 12.45 12.78 15.44* 13.55* 7.03 8.77 21.45*
-75/75/900 14.61 11.02 11.01 13.44 11.64 6.30 8.93* 16.84 17.93* 12.88 12.94 14.19 13.99 8.12 10.50* 21.37*
-75/300/600 14.50 11.20 10.90 13.79 11.29 5.82* 8.32 17.36 16.63 12.00 12.03 13.53 12.30 * 8.17 10.46* 20.38
-600/600/600 14.36 10.60 10.28 14.58 12.06 7.04 8.16 17.42 17.06 12.92* 13.60* 14.42 14.07 8.33 10.25* 20.70
-TABLE III
-IMPACT OF INDIVIDUAL LOSS COMPONENTS OF Ltotal ON THE PREDICTION ACCURACY ON MMWHS MR AND CT TEST DATASETS . * D ENOTES
-SIGNIFICANT DIFFERENCE OF OUR FINAL NETWORK MODEL "OURS (S)" F ROM THE OTHERS (P-VALUES <0.05)
-CT MR
-Models Myo LA LV RA RV Ao PA WH Myo LA LV RA RV Ao PA WH
-DCSOurs (S) 90.07 93.18 93.47 89.48 91.48 93.33 85.60 91.76 80.45 86.98 91.61 88.08 88.09 85.76 78.14 87.41
-w/o segmentation 87.11* 93.32 92.32* 88.74 90.65* 92.90 85.09 90.68* 77.96* 86.31 90.81* 87.48 87.03* 85.47 77.28 86.28*
-w/o L2 88.85* 92.94 92.57* 88.90* 90.96* 93.68 83.87* 91.10* 79.57* 85.96 90.56* 86.75* 87.01 84.46 75.00* 86.23*
-w/o L2+V 85.89* 93.18 92.34* 89.11 90.83* 93.41 85.38 90.57* 79.08* 86.92 91.93 87.51* 87.01 85.02 75.30* 86.66*
-w/o L2+S 86.12* 92.73* 90.84* 88.78* 90.41* 91.99* 83.81* 90.05* 78.11* 87.09 90.94* 87.63 87.34 85.04 76.77 86.58*
-HDOurs (S) 14.41 10.72 10.41 13.80 11.63 6.59 7.88 16.95 16.39 12.12 11.93 13.93 14.76 7.19 9.12 19.97
-w/o segmentation 14.41 9.98 10.44 15.04* 12.06 6.80 8.44 17.50 17.62 12.71 13.60* 14.57 15.70 7.89 8.91 20.73
-w/o L2 14.32 10.86 10.49 14.63* 12.39 6.34 8.38 17.26 16.54 11.84 11.46 14.74 14.75 8.17* 9.86 20.52
-w/o L2+V 14.42 10.41 10.27 14.43 12.34 6.30 6.78 * 17.55 17.09 12.47 11.79 14.40 14.39 7.51 9.93 21.19*
-w/o L2+S 14.17 12.20* 12.58* 16.31* 13.54* 8.17* 9.46* 17.82 16.65 13.72* 14.35* 16.19* 16.07* 8.91* 10.37* 20.74
-B. Construction of Cardiac Meshes for CFD Simulations
-TABLE IV
-ABLATION STUDY OF MESH REGULARIZATION LOSSES ON VESSEL INLET
-AND OUTLET STRUCTURES ON CT TEST DATASET (N=20).
-CoP+
-Ortho+HWCoP+Ortho CoP None
-Cap-Wall
-Orthogonality ( ↓)LA 0.128 ±0.121 0.032±0.012 0.365 ±0.265 0.273 ±0.266
-RA 0.023 ±0.008 0.012±0.008 0.105 ±0.038 0.066 ±0.026
-Ao 0.019 ±0.023 0.005±0.006 0.467 ±0.117 0.127 ±0.024
-Cap Coplanarity
-(mm) (↓)LA 0.228 ±0.041 0.256 ±0.029 0.12±0.024 0.312 ±0.058
-RA 0.34 ±0.073 0.339 ±0.055 0.185±0.043 0.466 ±0.114
-Ao 0.447 ±0.115 0.429 ±0.063 0.263±0.068 0.852 ±0.16
-Wall Chamfer
-Distance (mm) ( ↓)LA 2.093 ±0.803 2.715 ±1.105 2.487 ±0.898 2.042±0.857
-RA 2.021 ±1.176 2.66 ±1.301 2.231 ±0.983 1.899±0.952
-1) Ablation Study of Individual Loss Components on Vessel
-Inlet/Outlet Structures: CFD simulations of cardiac flow re-
-quires well-defined inlet and outlet vessel structures to pre-
-scribe boundary conditions for the inflow and outflow. Figure
-7 and table IV demonstrate the effect of applying individual
-regularization loss components on the predicted inlet and
-outlet geometries (pulmonary veins, vena cava, and aorta).
-Without any of the regularization losses, the predicted vessel
-structure lacked well define caps. Indeed, our ground truth
-meshes were generated from manual segmentations where
-vessels were not truncated precisely orthogonal to the vessel
-walls by the human observers, and the caps were not co-
-planar due to necessary smoothing steps to filter out the
-Fig. 7. Visualization of example whole heart surface predictions follow-
-ing addition of regularization losses on vessel inlet/outlet structures. The
-yellow regions highlight the ”caps” where the regularization losses were
-applied.
-staircase artifacts. The coplanar loss and the orthogonal loss
-succeeded in producing more planar cap geometries that were
-more orthogonal to vessel walls. Owning to the imperfect
-ground truth vessel meshes, although adding regularization
-losses to the training objective improved the structural quality
-of inlet geometries, it slightly reduced the geometric accuracy
-in terms of Chamfer distances compared with the ground truth.
-Applying a higher weight on the inlet mesh vertices in the
-geometric consistency loss was able to improve the geometricKONG et al. : LEARNING WHOLE HEART MESH GENERATION FROM PATIENT IMAGES FOR COMPUTATIONAL SIMULATIONS 9
-TABLE V
-COMPARISON OF DCS (↑)AND HDS(MM) (↓)OF PREDICTIONS
-FROM DIFFERENT METHODS ON 4D CT TEST IMAGES (N=20). *
-DENOTES SIGNIFICANT DIFFERENCE OF"OURS (S)" F ROM THE
-OTHERS (P<0.05)
-Myo LA LV RA RV Ao PA WH
-DiceOurs (S) 89.53 93.30 94.48 92.91 94.32 96.20 85.31 93.14
-Ours (V) 88.27* 91.60* 93.21* 92.18* 93.21* 95.62* 83.48* 91.97*
-HeartFFDNet 84.37* 88.38* 91.41* 90.26* 90.19* 93.03* 70.44* 88.94*
-MeshDeformNet 90.58 *95.18 *95.85 *93.50 94.63 *97.50 * 80.21* 93.94 *
-HD (mm)Ours (S) 6.04 10.21 4.95 10.04 6.61 3.80 19.71 16.02
-Ours (V) 5.91* 10.64 5.36 10.32 7.03* 4.16 19.14 15.69
-HeartFFDNet 6.78* 12.01* 6.37 10.85* 8.77* 5.13* 23.20 16.46
-MeshDeformNet 5.98 9.29 4.39 10.42 6.35*3.42 23.25 15.77
-accuracy of vessel inlets while maintaining satisfactory mesh
-quality for CFD simulations.
-Fig. 8. Qualitative comparison of whole heart surfaces from different
-methods at the end-diastolic phase and the end-systolic phase of a set
-of time-series image data. The colormap for end-systolic surfaces shows
-vertex displacement magnitude from end-systole to end-diastole.
-2) Comparison with Other Methods on Time-Series CT Data:
-Table V compares the reconstruction accuracy between our
-method and the other baseline methods on end-diastolic and
-end-systolic phases of a cardiac cycle. Overall, our method
-demonstrated high accuracy comparable to the prior state-
-of-the-art approach MeshDeformNet, both in terms of Dice
-scores and Hausdorff distances. Figure 8 shows a qualitative
-comparison of the reconstructed whole heart surfaces at end-
-systolic and end-diastolic phases and the estimated surface
-motion by computing the displacements of mesh vertices
-over time. MeshDeformNet produced gaps between cardiac
-structures as well as overly smoothed pulmonary veins and
-vena cava geometries, since that method is biased by the
-use of sphere templates rather than a more fitting template
-of the whole heart. In contrast, our method produced high-
-quality geometries of the vessel inlets and outlets as well
-as whole heart geometries that better match with the ground
-truth. Furthermore, our method demonstrated a more accurate
-estimation of surface deformation over time, which is required
-for prescribing boundary conditions for CFD simulations.
-Figure 9 provides further qualitative comparisons between
-using FFD and using biharmonic coordinates to deform the
-template. Using biharmonic coordinates enables more flexible
-Fig. 9. Comparison of whole heart surface predictions between using
-control handles as in our approach and using FFD as in HeartFFDNet.
-deformation and can thus more closely capture detailed ge-
-ometries such as the left atrial appendage. In contrast, geome-
-tries of left atrial appendage predicted from HeartFFDNet were
-strongly biased by the geometries of the template, although it
-used far more control points (4096) than our method (600).
-Furthermore, our method was able to predict cardiac structures
-that were not covered in the image data. Namely, thanks to
-the augmentation pipeline, our method generated reasonable
-geometries of the pulmonary arteries and pulmonary veins.
-In contrast, manual segmentation can only produce surface
-meshes of the cardiac structures captured in the images and
-HeartFFDNet predicted flat and unphysiological geometries
-despite starting from a realistic whole heart template.
-We further quantified the accuracy of the predicted shape
-of the heart outside the input image data. Since most images
-from the time-series CT dataset did not cover the entire
-heart, we selected 28 images that covered the entire heart
-from the MMWHS CT test dataset, and cropped them above
-various axial planes to evaluate the accuracy of the predicted
-whole heart reconstruction when increasing portions of cardiac
-structures were uncovered in the input images. Figure 10
-(top) compares the average point-to-point distance errors for
-each cardiac structures when the image volume was cropped
-along the axial view before and after applying the proposed
-random-cropping augmentation method and weighted losses
-on the meshes. When the random-cropping augmentation
-was applied, our method produced more accurate aorta and
-pulmonary arteries. As expected, the distance errors increased
-when more image data were removed. When as much as
-30% of the image volume was removed, the average distance
-errors of pulmonary arteries and aorta were around 1 cm with
-the augmentation, whereas the average distance errors were
-around 2.5 cm without the augmentation. Figure 10 (bottom)
-visualizes three examples of the reconstruction results where
-15%,22.5%, and 30% of the image data were removed. Our
-method produced reasonable geometries of the pulmonary
-arteries, pulmonary veins, and aorta in all cases. Although10 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-our method tended to predict a shorter aorta when the image
-data had limited coverage of the aorta, we note that the length
-of aortic outlet is often arbitrary when creating meshes for
-CFD simulations and thus does not significantly impact the
-simulation results.
-Fig. 10. Accuracy of shape completion of the heart when image data
-has limited coverage for aorta, pulmonary arteries and veins. Top: A
-comparison of point-to-point L2 distance between the meshes predicted
-using uncropped input images and using cropped input images at varies
-percentage, for network models trained without and with the random-
-cropping augmentation. Bottom: Example prediction results for three
-cases using uncropped input images and using cropped input images at
-varies percentage. The color map on the meshes for uncropped images
-indicates different cardiac structures whereas those for cropped images
-indicates L2 distance in mm.
-Table VI compares the quality of the predicted inlet and
-outlet geometries as well as the percentage face intersection of
-the whole heart meshes. Besides comparing with our baselines,
-HeartFFDNet and MeshDeformNet, we also compared our
-method with the surface meshes generated from applying the
-Marching Cube algorithm on manual ground truth segmen-
-tations, where the vessel inlet and outlet geometries were
-manually trimmed by human experts. Our method produced
-significantly better vessel inlet and outlet geometries than
-HeartFFDNet and MeshDeformNet. Also, our method outper-
-formed the manual segmentation in terms of Cap-Wall Or-
-thogonality. When deforming the mesh template using control
-handles, our method achieved the lowest percentage of face
-intersection than other deep-learning methods, and the small
-amount of face intersections that occurred could be readily
-corrected by a few iterations of Laplacian smoothing.C. CFD Simulations of Cardiac Flow
-We were able to successfully conduct CFD simulations
-using the automatically constructed LV meshes for all 10
-patients, as well as for 9 of 10 patients with the 4-chamber
-meshes. The 1 failed case had structure penetrations between
-two pulmonary veins, causing the simulation to diverge. Figure
-11 displays the simulation results of the velocity streamlines
-at multiple time steps during diastole for 2 different patients.
-The simulation results demonstrate the formation of typical
-vortex flow during ventricle filling.
-Fig. 11. Velocity streamlines from CFD simulations of 2 different
-patients using the predicted 4D meshes.
-Fig. 12. Quantitative comparisons of the % errors in LV volume, volume
-averaged KE density, mean velocity near the MO during diastole and
-mean velocity near the AO during systole among different methods.
-Lines show the mean values and shades show the 95% confidence
-intervals.
-Figure 12 provides quantitative comparisons of the ac-
-curacy of CFD simulation results of LV flow. Both our
-approach and HeartFFDNet significantly outperformed the
-image-registration-based approach in terms of all metrics.
-Namely, the image-registration-based method significantly un-
-derestimated the LV volume during diastole since the recon-
-structed meshes did not capture the large deformation of LV
-from systole to diastole. Our proposed approach demonstrated
-comparable or slightly better accuracy than HeartFFDNet in
-general, with smaller volume errors throughout the cardiac
-cycle and smaller errors in average kinetic energy and mean
-aortic flow velocity during systole.KONG et al. : LEARNING WHOLE HEART MESH GENERATION FROM PATIENT IMAGES FOR COMPUTATIONAL SIMULATIONS 11
-TABLE VI
-ACOMPARISON OF THE QUALITY OF THE INLET /OUTLET GEOMETRIES AND WHOLE HEART SURFACE QUALITY FROM DIFFERENT METHODS .
-Cap-Wall Orthogonality ( ↓) Cap Coplanarity (mm) ( ↓) % Face Intersection ( ↓)
-LA RA Ao LA RA Ao WH
-Ours (V) 0.038±0.046 0.013±0.007 0.013±0.012 0.22±0.024 0.284±0.044 0.292±0.088 0.018±0.022
-HeartFFDNet 0.137 ±0.08 0.228 ±0.182 0.494 ±0.386 0.398 ±0.068 0.45 ±0.125 0.949 ±0.557 0.262 ±0.191
-MeshDeformNet 0.106 ±0.104 0.044 ±0.038 0.209 ±0.117 1.145 ±0.165 0.917 ±0.379 0.36 ±0.216 0.034 ±0.068
-Manual 0.04 ±0.04 0.034 ±0.054 0.025 ±0.023 0.037 ±0.009 0.035 ±0.007 0.02 ±0.003 0.0 ±0.0
-Fig. 13. Qualitative comparisons of the simulated flow pattern from different methods at different time phases during a cardiac cycle for an example
-case. Left: Streamlines within the left ventricle models. Right: Contours of the velocity magnitude at the same clipping plane. Color map shows the
-velocity magnitude
-Figure 13 qualitatively compares the simulated LV flow
-pattern during both systole and diastole using meshes automat-
-ically constructed by our proposed approach and HeartFFD-
-Net, semi-automatically constructed by conventional image
-registration and manually constructed by human observers.
-Image registration underestimated the LV expansion from end
-systole to diastole, leading to underestimated flow velocity
-and disparate flow pattern compared with the ground truth.
-Both of our approaches generally produced similar vortex
-structures during diastole and converging flow during systole,
-with moderate differences in flow velocity and vortex locations
-compared with the ground truth.
-V. D ISCUSSION
-Automated image-based reconstruction of cardiac meshes is
-important for computational simulation of cardiac physiology.
-While deep-learning-based methods have demonstrated suc-
-cess in tasks such as image segmentation and registration, fewstudies have addressed the end-to-end learning between images
-and meshes for modeling applications. Furthermore, prior
-learning-based mesh reconstruction approaches suffer from
-a number of limitations such as using decoupled meshes of
-individual cardiac structures and assumed mesh topology, thus
-unable to directly support different cardiac simulations without
-additional efforts [14], [15]. We addressed this challenge
-herein using a novel approach that trains a neural network
-to learn the translation of a small set of control handles to
-deform the space enclosing a whole heart template to fit
-the cardiac structures in volumetric patient image data. Our
-method demonstrated promising whole-heart reconstruction
-accuracy and was able to generate simulation-ready meshes
-from time-series image data for CFD simulations of cardiac
-flow.
-Our approach achieved comparable geometric accuracy to
-the prior state-of-the-art whole heart mesh reconstruction
-method MeshDeformNet [15] while having the additional
-advantage of directly enabling various cardiac simulations. We12 IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. XX, NO. XX, XXXX 2020
-TABLE VII
-COMPARISON OF MODEL SIZE ,TRAINING AND TESTING TIME .
-Ours HeartFFDNet MeshDeformNet 2D UNet 3D UNet
-# of Parameters 8.7M 8.5M 16.8M 31.1M 18.6M
-Training Time 18 hrs 26 hrs 32 hrs 7 hrs 37 hrs
-Test Time 0.230s 0.177s 0.425s 1.555s 0.367s
-note that our approach used fewer parameters in the CNN
-encoder compared to MeshDeformNet (Table VII) and the use
-of biharmonic coordinates naturally ensures the smoothness of
-deformation without using explicit mesh regularization (e.g.,
-Laplacian and/or edge length loss constraints [15]). This is im-
-portant since mesh regularization schemes can complicate the
-optimization process [37], whereas we observed our approach
-to converge significantly faster than MeshDeformNet (18 vs
-32 hrs on a GTX2080Ti GPU).
-For CFD simulations requiring the time-dependent mo-
-tion of the heart over the cardiac cycle, our method has
-the advantage of deforming the template mesh in a tem-
-porally consistent manner, enabling automated construction
-of dynamic cardiac meshes within minutes on a standard
-desktop computer. Registration-based approaches, in contrast,
-often require test time optimizations that are computationally
-expensive and prone to local minimums, which often lead
-to inaccurate registration results such as underestimation of
-large deformation. Although deep-learning approaches have
-been proposed to speed-up the registration process [38], [39],
-large-deformation registration on cardiac images remains chal-
-lenging for learning-based approaches. The establishment of
-temporal feature correspondence of our method is due to
-similar features of time-series images naturally being encoded
-into similar feature vectors by the CNN encoders and does
-not require explicit training. Nevertheless, ground truth data
-of anatomical landmarks could be incorporated in the future
-during training to further improve the accuracy of feature
-correspondence across different time frames or patients.
-Blood flow simulations developed from our automated mesh
-generation process demonstrated circulatory flow patterns dur-
-ing diastole and converging flow patterns during systole in the
-ventricular cavity consistent with prior studies [2], [40]. How-
-ever, we observed an average of 15-25% error in the simulated
-mean velocity and kinetic energy, despite a promising mean
-LV Dice scores of 93% and an mean volume error of 6%. Such
-amount of volume error is consistent with the inter- and intra-
-observer variations of manual LV segmentation [10], [15].
-Indeed, simulation of blood flow is sensitive to uncertainties in
-geometry, such as inflow directions and vessel and/or LV wall
-smoothness [40], [41]. We plan to conduct intra- and inter-
-observer studies on the ground truth meshes to further un-
-derstand the relationship between prediction uncertainties and
-the accuracy of CFD simulations. Nevertheless, our approach
-is among the first to enable creation of simulation-suitable
-meshes from patient images. And our design of using template
-meshes and control handles could support shape editing and
-analysis to study the effect of geometric variations on CFD
-simulations.
-Our proposed method has the following limitations. First,
-the testing images we used from the benchmark MMWHStest dataset and the times-series CT dataset do not cover the
-full variations of cardiac abnormalities observed clinically. For
-example, our test datasets did not contain patients with con-
-genital heart defects and thus the performance of our trained
-network for those patients were not evaluated. Furthermore,
-image data used to training and testing using relatively similar
-imaging protocols and parameters, such as slice thickness,
-field of view, and spatial orientation. The above limitations
-can be addressed by retraining the model with data more
-representative of particular use cases (e.g. particular abnor-
-malities or scanner protocol). A related limitation is that the
-whole-heart template used may need to be modified to capture
-richer variations of cardiac malformations. For example, the
-current template assumes four separate and distinct pulmonary
-vein ostia and thus may not fully capture pulmonary veins
-with alternate branching patterns, which can be important
-for preoperative planning of pulmonary and cardiac surgery
-[42]. Similarly, the template used would not be suitable for
-cardiac malformations such as single-ventricle patients with
-congenital heart diseases since the structures of the heart
-are significantly different from our current training template.
-Nonetheless, this framework could still be utilized if sufficient
-training data of, say, single-ventricle patients were available
-and a corresponding single-ventricle mesh template were used.
-To better handle the above applications, in future work we aim
-to add a template retrieval module to automatically select a
-template that best suits the application. Furthermore, implicit
-shape representation [43] can be combined with our learning-
-based shape deformation approach to predict cardiac structures
-with different anatomies.
-VI. C ONCLUSION
-We proposed a novel deep-learning approach that directly
-constructs simulation-ready whole heart meshes from cardiac
-image data and allows switching of template meshes to
-accommodate different modeling requirements. Our method
-leverages a graph convolutional network to predict the trans-
-lations of a small set of control handles to smoothly deform
-a whole heart template using biharmonic coordinates. Our
-method consistently outperformed prior state-of-the-art meth-
-ods in constructing simulation-ready meshes of the heart, and
-was able to produce geometries that better satisfy modeling
-requirements for cardiac flow simulations. We demonstrated
-application of our method on constructing dynamic whole
-heart meshes from time-series CT image data to simulate the
-ventricular flow driven by the cardiac motion. The presented
-approach is able to automatically construct whole heart meshes
-within seconds on a modern desktop computer and has the
-potential in facilitating high-throughput, large-cohort valida-
-tion of patient-specific cardiac modeling, as well as its future
-clinical applications.
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txt/2204.14047.txt

@@ -1,944 +0,0 @@
-A Deep Learning based No-reference Quality Assessment Model
-for UGC Videos
-Wei Sun
-Shanghai Jiao Tong University
-Shanghai, China
-[email protected] Min
-Shanghai Jiao Tong University
-Shanghai, China
-[email protected]
-Wei Lu
-Shanghai Jiao Tong University
-Shanghai, China
-[email protected] Zhai∗
-Shanghai Jiao Tong University
-Shanghai, China
-[email protected]
-ABSTRACT
-Quality assessment for User Generated Content (UGC) videos plays
-an important role in ensuring the viewing experience of end-users.
-Previous UGC video quality assessment (VQA) studies either use
-the image recognition model or the image quality assessment (IQA)
-models to extract frame-level features of UGC videos for quality
-regression, which are regarded as the sub-optimal solutions be-
-cause of the domain shifts between these tasks and the UGC VQA
-task. In this paper, we propose a very simple but effective UGC
-VQA model, which tries to address this problem by training an
-end-to-end spatial feature extraction network to directly learn the
-quality-aware spatial feature representation from raw pixels of the
-video frames. We also extract the motion features to measure the
-temporal-related distortions that the spatial features cannot model.
-The proposed model utilizes very sparse frames to extract spatial
-features and dense frames (i.e. the video chunk) with a very low
-spatial resolution to extract motion features, which thereby has
-low computational complexity. With the better quality-aware fea-
-tures, we only use the simple multilayer perception layer (MLP)
-network to regress them into the chunk-level quality scores, and
-then the temporal average pooling strategy is adopted to obtain
-the video-level quality score. We further introduce a multi-scale
-quality fusion strategy to solve the problem of VQA across differ-
-ent spatial resolutions, where the multi-scale weights are obtained
-from the contrast sensitivity function of the human visual system.
-The experimental results show that the proposed model achieves
-the best performance on five popular UGC VQA databases, which
-demonstrates the effectiveness of the proposed model. The code is
-available at https://github.com/sunwei925/SimpleVQA.
-CCS CONCEPTS
-•Computing methodologies →Modeling methodologies .
-∗Corresponding author: Guangtao Zhai.
-Permission to make digital or hard copies of all or part of this work for personal or
-classroom use is granted without fee provided that copies are not made or distributed
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-to post on servers or to redistribute to lists, requires prior specific permission and/or a
-fee. Request permissions from [email protected].
-MM ’22, October 10–14, 2022, Lisboa, Portugal
-©2022 Association for Computing Machinery.
-ACM ISBN 978-1-4503-9203-7/22/10. . . $15.00
-https://doi.org/10.1145/3503161.3548329KEYWORDS
-video quality assessment, UGC videos, deep learning, feature fusion
-ACM Reference Format:
-Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai∗. 2022. A Deep Learn-
-ing based No-reference Quality Assessment Model for UGC Videos. In Pro-
-ceedings of the 30th ACM International Conference on Multimedia (MM ’22),
-October 10–14, 2022, Lisboa, Portugal. ACM, New York, NY, USA, 10 pages.
-https://doi.org/10.1145/3503161.3548329
-1 INTRODUCTION
-With the proliferation of mobile devices and wireless networks in
-recent years, User Generated Content (UGC) videos have exploded
-over the Internet. It has become a popular daily activity for the gen-
-eral public to create, view, and share UGC videos through various
-social media applications such as YouTube, TikTok, etc. However,
-UGC videos are captured by a wide variety of consumers, ranging
-from professional photographers to amateur users, which makes
-the visual quality of UGC videos vary greatly. In order to ensure
-the Quality of Experience (QoE) of end-users, the service providers
-need to monitor the quality of UGC videos in the entire streaming
-media link, including but not limited to video uploading, compress-
-ing, post-processing, transmitting, etc. Therefore, with billions of
-video viewing and millions of newly uploaded UGC videos every
-day, an effective and efficient video quality assessment (VQA) model
-is needed to measure the perceptual quality of UGC videos.
-Objective VQA can be divided into full-reference (FR), reduced-
-reference (RR), and no-reference (NR) according to the amount of
-pristine video information needed. Since there is no reference video
-for in-the-wild UGC videos, only NR VQA models are qualified for
-evaluating their quality. Although NR VQA algorithms [ 21,23,26]
-have been studied for many years, most of them were developed
-for Professionally Generated Content (PGC) videos with synthetic
-distortions, where the pristine PGC videos are shot by photogra-
-phers using professional devices and are normally of high quality,
-and the distorted PGC videos are then degraded by specific video
-processing algorithms such as video compression, transmission, etc.
-So, previous VQA studies mainly focus on modeling several types
-of distortions caused by specific algorithms, which makes them less
-effective for UGC videos with in-the-wild distortions. To be more
-specific, the emerging UGC videos pose the following challenges
-to the existing VQA algorithms for PGC videos:arXiv:2204.14047v2  [cs.CV]  20 Oct 2022MM ’22, October 10–14, 2022, Lisboa, Portugal Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai∗
-First, the distortion types of UGC videos are diverse. A mass
-of UGC videos are captured by amateur users, which may suffer
-various distortion types such as under/over exposure, low visibility,
-jitter, noise, color shift, etc. These authentic distortions are intro-
-duced in the shooting processing and cannot be modeled by the
-single distortion type, which thereby requires that the VQA models
-have a more strong feature representation ability to qualify the
-authentic distortions. Second, the content and forms of UGC videos
-are extremely rich. UGC videos can be natural scenes, animation
-[35], games [ 46,47], screen content, etc. Note that the statistics
-characteristics of different video content vary greatly. For example,
-the natural scenes statistics (NSS) features [ 22–24,26] are com-
-monly used in the previous VQA studies to measure the distortions
-of natural scene content, but they may be ineffective for computer-
-generated content like animation or games. In addition, live videos,
-videoconferencing, etc. are also ubiquitous for UGC videos nowa-
-days, whose quality is severely affected by the network bandwidth.
-Third, due to the advancement of shooting devices, more high res-
-olution [ 18] and high frame rate [ 19,52,53] videos have emerged
-on the Internet. The various kinds of resolutions and frame rates
-are also important factors for video quality. What’s more, users
-can view the UGC videos through mobile devices anywhere and at
-any time, so the display [ 25] and the viewing environment such as
-ambient luminance [ 29], etc. also affect the perceptual quality of
-UGC videos to a certain extent. However, these factors are rarely
-considered by previous studies.
-The recently released large-scale UGC VQA databases such as
-KoNViD-1k [ 8], YouTube UGC [ 36], LSVQ [ 44], etc. have greatly
-promoted the development of UGC VQA. Several deep learning
-based NR VQA models [ 14,15,37,40,44] have been proposed to
-solve some challenges mentioned above and achieve pretty good
-performance. However, there are still some problems that need to
-be addressed. First, the previous studies either use the image recog-
-nition model [ 15][44] or the pretrained image quality assessment
-(IQA) models [ 37][40][14] to extract frame-level features, which
-lacks an end-to-end learning method to learn the quality-aware
-spatial feature representation from raw pixels of video frames. Sec-
-ond, previous studies usually extract the features from all video
-frames and have a very high computational complexity, making
-them difficult to apply to real-world scenarios. Since there is much
-redundancy spatial information between the adjacent frames, we
-argue that there is not necessary to extract the features from all
-frames. Third, the spatial resolution and frame rate of UGC videos
-as well as other factors such as the display, viewing environment,
-etc. are still rarely considered by these studies. However, these
-factors are very important for the perceptual quality of UGC videos
-since the contrast sensitivity of the human visual system (HVS) is
-affected by them.
-In this paper, to address the challenges mentioned above, we
-propose a very simple but effective deep learning based VQA model
-for UGC videos. The proposed framework is illustrated in Figure 1,
-which consists of the feature extraction module, the quality regres-
-sion module, and the quality pooling module. For the feature ex-
-traction module, we extract quality-aware features from the spatial
-domain and the spatial-temporal domain to respectively measure
-the spatial distortions and motion distortions. Instead of using thepretrained model to extract the spatial features in the previous stud-
-ies, we propose to train an end-to-end spatial feature extraction
-network to learn quality-aware feature representation in the spatial
-domain, which thereby makes full use of various video content
-and distortion types in current UGC VQA databases. We then uti-
-lize the action recognition network to extract the motion features,
-which can make up the temporal-related distortions that the spatial
-features cannot model. Considering that the spatial features are
-sensitive to the resolution while the motion features are sensitive
-to the frame rate, we first split the video into continuous chunks
-and then extract the spatial features and motion features by using a
-key frame of each chunk and all frames of each chunk but at a low
-spatial resolution respectively. So, the computational complexity of
-the proposed model can be greatly reduced.
-For the quality regression module, we use the multilayer per-
-ception (MLP) network to map the quality-aware features into
-the chunk-level quality scores, and the temporal average pooling
-strategy is adopted to obtain the final video quality. In order to
-solve the problem of quality assessment across different resolu-
-tions, we introduce a multi-scale quality fusion strategy to fuse
-the quality scores of the videos with different resolutions, where
-the multi-scale weights are obtained from the contrast sensitivity
-function (CSF) of HVS by considering the viewing environment
-information. The proposed models are validated on five popular
-UGC VQA databases and the experimental results show that the
-proposed model outperforms other state-of-the-art VQA models
-by a large margin. What’s more, the proposed model trained on a
-large-scale database such as LSVQ [ 44] achieves remarkable perfor-
-mance when tested on the other databases without any fine-tuning,
-which further demonstrates the effectiveness and generalizability
-of the proposed model.
-In summary, this paper makes the following contributions:
-(1)We propose an effective and efficient deep learning based
-model for UGC VQA, which includes the feature extraction
-module, the quality regression module, and the quality pool-
-ing module. The proposed model not only achieves remark-
-able performance on the five popular UGC VQA databases
-but also has a low computational complexity, which makes
-it very suitable for practical applications.
-(2)The feature extraction module extracts two kinds of quality-
-aware features, the spatial features for spatial distortions and
-the spatial-temporal features for motion distortions, where
-the spatial features are learned from raw pixels of video
-frames via an end-to-end manner and the spatial-temporal
-features are extracted by a pretrained action recognition
-network.
-(3)We introduce a multi-scale quality fusion strategy to solve
-the problem of quality assessment across different resolu-
-tions, where the multi-scale weights are obtained from the
-contrast sensitivity function of the human visual system by
-considering the viewing environment information.
-2 RELATED WORK
-2.1 Handcrafted feature based NR VQA Models
-A naive NR VQA method is to compute the quality of each frame
-via popular NR IQA methods such as NIQE [ 24], BRISQUE [ 22],A Deep Learning based No-reference Quality Assessment Model for UGC Videos MM ’22, October 10–14, 2022, Lisboa, Portugal
-Input video
-Frame extraction
-2D framesChunks
-Global Average and STD Pooling
-Spatial feature extractionStage 1Stage 2 Stage 3 Stage 4Motion feature extraction
-Quality regressionQuality
-ScoreMotion feature Spatial feature
-Pooling3D CNN
-Figure 1: The network architecture of the proposed model. The proposed model contains the feature extraction module, the
-quality regression module, and the quality pooling module. The feature extraction module extracts two kinds of features, the
-spatial features and the motion features.
-CORNIA [ 42] etc., and then pool them into the video quality score.
-A comparative study of various temporal pooling strategies on pop-
-ular NR IQA methods can refer to [ 32]. The temporal information
-is very important for VQA. V-BLIINDS [ 26] is a spatio-temporal
-natural scene statistics (NSS) model for videos by quantifying the
-NSS feature of frame-differences and motion coherency character-
-istics. Mittal et al. [23] propose a training-free blind VQA model
-named VIIDEO that exploits intrinsic statistics regularities of natu-
-ral videos to quantify disturbances introduced due to distortions.
-TLVQM [ 12] extracts abundant spatio-temporal features such as
-motion, jerkiness, blurriness, noise, blockiness, color, etc. at two
-levels of high and low complexity. VIDEVAL [ 33] further combines
-the selected features from typical NR I/VQA methods to train a SVR
-model to regress them into the video quality. Since video content
-also affects its quality, especially for UGC videos, understanding the
-video content is beneficial to NR VQA. Previous handcrafted feature
-based methods are difficult to understand semantic information.
-Hence, some studies [ 13,34] try to combine the handcrafted features
-with the semantic-level features extracted by the pretrained CNN
-model to improve the performance of NR VQA models. For example,
-CNN-TLVQM [ 13] combines the handcrafted statistical temporal
-features from TLVQM and spatial features extracted by 2D-CNN
-model trained for IQA. RAPIQUE [ 34] utilizes the quality-aware
-scene statistics features and semantics-aware deep CNN features
-to achieve a rapid and accurate VQA model for UGC videos.
-2.2 Deep learning based NR VQA Models
-With the release of several large-scale VQA databases [ 8,36,44],
-deep learning based NR VQA models [ 2,11,14,15,31,37,40,43,44]
-attract many researchers’ attention. Liu et al. [17] propose a multi-
-task BVQA model V-MEON by jointly optimizing the 3D-CNN
-for quality assessment and compression distortion classification.
-VSFA [ 15] first extracts the semantic features from a pre-trained
-CNN model and then uses a gated recurrent unit (GRU) network
-to model the temporal relationship between the semantic features
-of video frames. The authors of VSFA further propose MDVSFA[16], which trains the VSFA model on the multiple VQA databases
-to improve its performance and generalization. RIRNet [ 4] exploits
-the effect of motion information extracted from the multi-scale
-temporal frequencies for video quality assessment. Ying et al. [44]
-propose a local-to-global region-based NR VQA model that com-
-bines the spatial features extracted from a 2D-CNN model and the
-spatial-temporal features from a 3D-CNN network. Wang et al. [37]
-propose a feature-rich VQA model for UGC videos, which measures
-the quality from three aspects, compression level, video content,
-and distortion type and each aspect is evaluated by an individual
-neural network. Xu et al. [40] first extract the spatial feature of
-the video frame from a pre-trained IQA model and use the graph
-convolution to extract and enhance these features, then extract
-motion information from the optical flow domain, and finally inte-
-grated the spatial feature and motion information via a bidirectional
-long short-term memory network. Li et al. [14] also utilize the IQA
-model pre-trianed on multiple databases to extract quality-aware
-spatial features and the action recognition model to extract tem-
-poral features, and then a GRU network is used to model spatial
-and temporal features and regress them into the quality score. Wen
-and Wang [ 39] propose a baseline I/VQA model for UGC videos,
-which calculates the video quality by averaging the scores of each
-frame and frame-level quality scores are obtained by a simple CNN
-network.
-3 PROPOSED MODEL
-The framework of the proposed NR VQA model is shown in Fig-
-ure 1, which consists of the feature extraction module, the quality
-regression module, and the quality pooling module. First, we ex-
-tract the quality-aware features from the spatial domain and the
-spatial-temporal domain via the feature extraction module, which
-are utilized to evaluate the spatial distortions and motion distor-
-tions respectively. Then, the quality regression module is used to
-map the quality-aware features into chunk-level quality scores. Fi-
-nally, we perform the quality pooling module to obtain the video
-quality score.MM ’22, October 10–14, 2022, Lisboa, Portugal Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai∗
-3.1 Feature Extraction Module
-In this section, we expect to extract the quality-aware features that
-can represent the impact of various distortion types and content
-on visual quality. The types of video distortion can be roughly
-divided into two categories: the spatial distortions and the motion
-distortions. The spatial distortions refer to the artifacts introduced
-in the video frames, such as noise, blur, compression, low visibility,
-etc. The motion distortions refer to the jitter, lagging due, etc., which
-are mainly caused by unstable shooting equipment, fast-moving
-objects, the low network bandwidth, etc. Therefore, we need to
-extract the quality-aware features from these two aspects.
-Note that the characteristics of the spatial features and motion
-features are quite different. The spatial features are sensitive to
-the video resolution but insensitive to the video frame rate since
-the adjacent frames of the video contain lots of redundancy spatial
-information and higher resolution can represent more abundant
-high-frequency information, while motion features are the opposite
-because the motion distortions are reflected on the temporal dimen-
-sion and these features are usually consistent for local regions of
-the frames.
-Therefore, considering these characteristics, given a video 𝑉,
-whose number of frames and frame rate are 𝑙and𝑟respectively,
-we first split the video 𝑉into𝑁𝑐continuous chunks 𝑐={𝑐𝑖}𝑁𝑐
-𝑖=1at
-an time interval 𝜏, where𝑁𝑐=𝑙/(𝑟∗𝜏), and there are 𝑁𝑓=𝑟∗𝜏
-frames in each chunk 𝑐𝑖, which is denoted as 𝑐𝑖={𝑥𝑖,𝑗}𝑁𝑓
-𝑗=1. Then
-we only choose a key frame 𝑥𝑖,𝑘𝑒𝑦 in each chunk to extract the
-spatial features and the motion features of each chunk are extracted
-using all frames in 𝑐𝑖but at a very low spatial resolution. As a
-result, we can greatly reduce the computation complexity of the
-VQA model with little performance degradation.
-3.1.1 Spatial Feature Extraction Module. Given a frame 𝑥, we de-
-note𝑓𝑤(𝑥)as the output of the CNN model 𝑓with trainable pa-
-rameters𝑤={𝑤𝑘}applied on the frame 𝑥. Assume that there are
-𝑁𝑠stages in the CNN model, and 𝑓𝑘𝑤(𝑥)is the output feature maps
-extracted from the 𝑘-th stage, where 𝑓𝑘𝑤(𝑥)∈R𝐻𝑘×𝑊𝑘×𝐶𝑘, and𝐻𝑘,
-𝑊𝑘, and𝐶𝑘are the height, width, and the number of channels of
-the feature maps 𝑓𝑘𝑤(𝑥)respectively. In the following, we use the
-𝑓𝑘𝑤to replace the 𝑓𝑘𝑤(𝑥)for simplicity.
-It is well known that the features extracted by the deep lay-
-ers of the CNN model contain rich semantic information, and are
-suitable for representing content-aware features for UGC VQA.
-Moreover, previous studies indicate that the features extracted by
-the shallow layers of the CNN models contain low-level informa-
-tion [ 28,48], which responds to low-level features such as edges,
-corners, textures, etc. The low-level information is easily affected
-by the distortion and is therefore distortion-aware. Hence, we ex-
-tract the quality-aware features via calculating the global mean
-and stand deviation of feature maps extracted from all stages of the
-CNN model. Then, we apply global average and stand deviation
-pooling operations on the feature maps 𝑓𝑘𝑤:
-𝜇𝑓𝑘𝑤=GPavg(𝑓𝑘
-𝑤),
-𝜎𝑓𝑘𝑤=GPstd(𝑓𝑘
-𝑤),(1)where𝜇𝑓𝑘𝑤and𝜎𝑓𝑘𝑤are the global means and stand deviation of
-feature maps 𝑓𝑘𝑤respectively. Finally, we concatenate the 𝜇𝑓𝑘𝑤and
-𝜎𝑓𝑘𝑤to derive the spital feature representation of our NR VQA model:
-𝐹𝑘
-𝑠=cat([𝜇𝑓𝑘𝑤,𝜎𝑓𝑘𝑤]),
-𝐹𝑠=cat({𝐹𝑘
-𝑠}𝑁𝑠
-𝑘=1).(2)
-3.1.2 Motion Feature Extraction Module. We extract the motion
-features as the complementary quality-aware features since UGC
-videos are commonly degraded by the motion distortions caused
-by the unstable shooting equipment or low bit rates in the living
-streaming or videoconferencing. The spatial features are difficult
-to handle these distortions because they are extracted by the intra-
-frames while motion distortions occur in the interframes. Therefore,
-the motion features are also necessary for evaluating the quality
-of UGC videos. Here, we utilize the pretrained action recognition
-model as the motion feature extractor to obtain the motion features
-of each video chunk. The action recognition model is designed
-to detect different kinds of action classes, so the feature represen-
-tation of the action recognition network can reflect the motion
-information of the video to a certain extent. Therefore, given the
-video chunk 𝑐and the action recognition network MOTION , we
-can obtain the motion features:
-𝐹𝑚=MOTION(c) (3)
-where𝐹𝑚represents the motion features extract by the action
-recognition network.
-Therefore, given the video chunk 𝑐, we first select a key frame
-in the chunk to calculate the spatial features 𝐹𝑠. Then, we calculate
-the motion features 𝐹𝑚using the whole frames but at a low spatial
-resolution in the video chunk. Finally, we obtain the quality-aware
-features for the video chunk 𝑐by concatenating the spatial features
-and motion features:
-𝐹=cat([𝐹𝑠,𝐹𝑚]), (4)
-3.2 Quality Regression Module
-After extracting quality-aware feature representation by the feature
-extraction module, we need to map these features to the quality
-scores via a regression model. In this paper, we use the multi-layer
-perception (MLP) as the regression model to obtain the chunk-level
-quality due to its simplicity and effectiveness. The MLP consists of
-two fully connected layers and there are 128 and 1 neuron in each
-layer respectively. Therefore, we can obtain the chunk-level quality
-score via
-𝑞=𝑓𝑤FC(𝐹), (5)
-where𝑓𝑤FCdenotes the function of the two FC layers and 𝑞is the
-quality of the video chunk.
-3.3 Quality Pooling Module
-As stated in Section 3.1, we split the video 𝑉into𝑁𝑐continuous
-chunks{𝑐𝑖}𝑁𝑐
-𝑖=1. For the chunk 𝑐𝑖, we can obtain its chunk-level
-quality score 𝑞𝑖via the feature extraction module and the quality
-regression module. Then, it is necessary to pool the chunk-level
-scores into the video level. Though many temporal pooling methods
-have been proposed in literature [ 32][15], we find that the temporalA Deep Learning based No-reference Quality Assessment Model for UGC Videos MM ’22, October 10–14, 2022, Lisboa, Portugal
-Table 1: Summary of the benchmark UGC VQA databases. Time duration: Seconds.
-Database Videos Scenes Resolution Time Duration Format Distortion Type DATA Environment
-KoNViD-1k [8] 1,200 1,200 540p 8 MP4 Authentic MOS + 𝜎 Crowd
-YouTube-UGC [36] 1500 1500 360p-4K 20 YUV, MP4 Authentic MOS + 𝜎 Crowd
-LSVQ [44] 38,811 38,811 99p-4K 5-12 MP4 Authentic MOS + 𝜎 Crowd
-LBVD [3] 1,013 1,013 240p-540p 10 MP4 Authentic, Transmission MOS + 𝜎 In-lab
-LIVE-YT-Gaming [45] 600 600 360p-1080p 8-9 MP4 Authentic MOS Crowd
-averaging pooling achieves the best performance from Section 4.3.2.
-Therefore, the video-level quality is calculated as:
-𝑄=1
-𝑁𝑐𝑁𝑐∑︁
-𝑖=1𝑞𝑖, (6)
-where𝑞𝑖is the quality of the 𝑖-th chunk and 𝑄is the video quality
-evaluated by the proposed model.
-3.4 Loss Function
-The loss function used to optimize the proposed models consists of
-two parts: the mean absolute error (MAE) loss and rank loss [ 39].
-The MAE loss is used to make the evaluated quality scores close to
-the ground truth, which is defined as:
-𝐿𝑀𝐴𝐸=1
-𝑁𝑁∑︁
-𝑖=1𝑄𝑖−ˆ𝑄𝑖, (7)
-where the ˆ𝑄𝑖is the ground truth quality score of the 𝑖-th video in a
-mini-batch and 𝑁is the number of videos in the mini-batch.
-The rank loss is further introduced to make the model distinguish
-the relative quality of videos better, which is very useful for the
-model to evaluate the videos with similar quality. Since the rank
-value between two video quality is non-differentiable, we use the
-following formula to approximate the rank value:
-𝐿𝑖𝑗
-𝑟𝑎𝑛𝑘=max(0,ˆ𝑄𝑖−ˆ𝑄𝑗−𝑒(ˆ𝑄𝑖,ˆ𝑄𝑗)·(𝑄𝑖−𝑄𝑗)), (8)
-where𝑖and𝑗are two video indexes in a mini-batch, and 𝑒(ˆ𝑄𝑖,ˆ𝑄𝑗)
-is formulated as:
-𝑒(ˆ𝑄𝑖,ˆ𝑄𝑗)=(
-1,ˆ𝑄𝑖≥ˆ𝑄𝑗,
-−1,ˆ𝑄𝑖<ˆ𝑄𝑗,(9)
-Then,𝐿𝑟𝑎𝑛𝑘 is calculated via:
-𝐿𝑟𝑎𝑛𝑘=1
-𝑁2𝑁∑︁
-𝑖=1𝑁∑︁
-𝑗=1𝐿𝑖𝑗
-𝑟𝑎𝑛𝑘(10)
-Finally, the loss function can be obtained by:
-𝐿=𝐿𝑀𝐴𝐸+𝜆·𝐿𝑟𝑎𝑛𝑘, (11)
-where𝜆is a hyper-parameter to balance the MAE loss and the rank
-loss.
-3.5 Multi-scale Quality Fusion Strategy
-Previous studies evaluate the video quality either using the original
-spatial resolution or a fixed resized spatial resolution, which ig-
-nore that videos are naturally multi-scale [ 53]. Some existing work
-[38][25][20] shows that considering the multi-scale characteristics
-can improve the performance of image quality assessment. So, wepropose a multi-scale quality fusion strategy to further improve
-the evaluation accuracy of the VQA model and this strategy is
-very useful to compare the quality of videos with different spatial
-resolutions.
-3.5.1 Multi-scale Video Quality Scores. We first resize the resolu-
-tion of the video into three fixed spatial scales, which are 540p,
-720p, and 1080p, respectively. We do not downscale the video from
-the original scale to several lower resolution scales, which is a
-more common practice in previous studies. That is because when
-users watch videos in an application, the resolution of videos is
-actually adapted to the resolution of the playback device, and the
-modern display resolution is normally larger than 1080p. So, the
-perceptual quality of the low-resolution videos is also affected by
-the up-sampling artifacts, which also need to be considered by VQA
-models. Therefore, given a VQA model, we can derive three qual-
-ity of videos at three scales, which are denoted as 𝑄1,𝑄2, and𝑄3
-respectively.
-3.5.2 Adaptive Multi-scale Weights. The weight of each scale is
-obtained by considering the human psychological behaviors and
-the visual sensitivity characteristics. It is noted that the contrast
-perception ability of the HVS depends on the spatial frequency
-of the visual signal, which is modeled by the contrast sensitivity
-function (CSF). Specifically, we first define a viewing resolution
-factor𝜉as:
-𝜉=𝜋·𝑑·𝑛
-180·ℎ𝑠·2, (12)
-where the unit of 𝜉is cycles per degree of visual angle (cpd), 𝑑is
-the viewing distance (inch), ℎ𝑠is the height of the screen (inch),
-and𝑛denotes the number of pixels in the vertical direction of the
-screen. For the above three spatial scales of video, we can obtain the
-corresponding 𝜉, which are denoted as 𝜉1,𝜉2, and𝜉3respectively.
-We use𝜉to divide the spatial frequency range of the corresponding
-scale, which covers one section of the CSF formulated by:
-𝑆(𝑢)=5200𝑒(−0.0016𝑢2(1+100/𝐿)0.08)
-√︃
-(1+144
-𝑋2
-0+0.64𝑢2)(63
-𝐿0.83+1
-1−𝑒(−0.02𝑢2))(13)
-where𝑢,𝐿, and𝑋2
-0indicate spatial frequency (cpd), luminance
-(cd/m2), and angular object area (squared degrees), respectively.
-The weight of each scale is calculated as the area under the CSF
-within the corresponding frequency covering range:
-𝑤𝑖=1
-𝑍∫𝜉𝑖
-𝜉𝑖−1𝑆(𝑢)d𝑢,𝑖∈{1,2,3}, (14)
-where𝑖from 1 to 3 corresponds the finest to coarsest scale respec-
-tively, and𝜉0corresponds the viewing resolution factor of 0. 𝑍is a
-normalization factor such thatÍ
-𝑖𝑤𝑖=1.MM ’22, October 10–14, 2022, Lisboa, Portugal Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai∗
-Table 2: Performance of the SOTA models and the proposed model on the KoNViD-1k, YouTube-UGC, LBVD, and LIVE-YT-
-Gaming databases. W.A. means the weight average results. The best performing model is highlighted in each column.
-TypeDatabase KoNViD-1k YouTube-UGC LBVD LIVE-YT-Gaming W.A.
-Criterion SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC SRCC PLCC
-IQANIQE 0.542 0.553 0.238 0.278 0.327 0.387 0.280 0.304 0.359 0.393
-BRISQUE 0.657 0.658 0.382 0.395 0.435 0.446 0.604 0.638 0.513 0.525
-GM-LOG 0.658 0.664 0.368 0.392 0.314 0.304 0.312 0.317 0.433 0.440
-VGG19 0.774 0.785 0.703 0.700 0.676 0.673 0.678 0.658 0.714 0.712
-ResNet50 0.802 0.810 0.718 0.710 0.715 0.717 0.729 0.768 0.744 0.751
-KonCept512 0.735 0.749 0.587 0.594 0.626 0.636 0.643 0.649 0.650 0.660
-VQAV-BLIINDS 0.710 0.704 0.559 0.555 0.527 0.558 0.357 0.403 0.566 0.578
-TLVQM 0.773 0.769 0.669 0.659 0.614 0.590 0.748 0.756 0.699 0.689
-VIDEVAL 0.783 0.780 0.779 0.773 0.707 0.697 0.807 0.812 0.766 0.762
-RAPIQUE 0.803 0.818 0.759 0.768 0.712 0.725 0.803 0.825 0.767 0.781
-VSFA 0.773 0.775 0.724 0.743 0.622 0.642 0.776 0.801 0.721 0.736
-Liel al. 0.836 0.834 0.831 0.819 - - - - - -
-Pro. 0.856 0.860 0.847 0.856 0.844 0.846 0.861 0.866 0.851 0.856
-Table 3: Performance of the SOTA models and the proposed
-models on the LSVQ database. Pro. M.S. refers to the pro-
-posed model implemented by the multi-scale quality fusion
-strategy. W.A. means the weighted average results. The best
-performing model is highlighted in each column.
-Database Test Test-1080p W.A.
-Criterion SRCC PLCC SRCC PLCC SRCC PLCC
-TLVQM 0.772 0.774 0.589 0.616 0.712 0.722
-VIDEVAL 0.794 0.783 0.545 0.554 0.712 0.707
-VSFA 0.801 0.796 0.675 0.704 0.759 0.766
-PVQ 0.827 0.828 0.711 0.739 0.789 0.799
-Liel al. 0.852 0.854 0.772 0.788 0.825 0.832
-Pro. 0.864 0.861 0.756 0.801 0.829 0.841
-Pro. M.S. 0.867 0.861 0.764 0.803 0.833 0.842
-Therefore, the multi-scale fusion quality score 𝑄𝑚is calculated
-as:
-𝑄𝑚=3Ö
-𝑖=1𝑄𝑤𝑖
-𝑖, (15)
-4 EXPERIMENTAL VALIDATION
-4.1 Experimental Protocol
-4.1.1 Test Databases. We test the proposed model on the five UGC
-VQA database: KoNViD-1k [ 8], YouTube-UGC [ 36], LSVQ [ 44],
-LBVD [ 3], and LIVE-YT-Gaming [ 45]. We summarize the main infor-
-mation of the databases in Table 1. The LSVQ database is the largest
-UGC VQA database so far, and there are 15 video categories such
-as animation, gaming, HDR, live music, sports, etc. in the YouTube-
-UGC database, which is more diverse than other databases. The
-LBVD database focuses on the live broadcasting videos, of which
-the videos are degraded by the authentic transmission distortions.
-The LIVE-YT-Gaming database consists of streamed gaming videos,
-where the video content is generated by computer graphics.4.1.2 Implementation Details. We use the ResNet50 [ 7] as the back-
-bone of the spatial feature extraction module and the SlowFast R50
-[6] as the motion feature extraction model for the whole experi-
-ments. The weights of the ResNet50 are initialized by training on
-the ImageNet dataset [ 5], the weights of the SlowFast R50 are fixed
-by training on the Kinetics 400 dataset [ 10], and other weights are
-randomly initialized. For the spatial feature extraction module, we
-resize the resolution of the minimum dimension of key frames as
-520 while maintaining their aspect ratios. In the training stage, the
-input frames are randomly cropped with the resolution of 448 ×448.
-If we do not use the multi-scale quality fusion strategy, we crop the
-center patch with the same resolutions of 448 ×448 in the testing
-stage. Note that we only validate the multi-scale quality fusion
-strategy on the model trained by the LSVQ database since there
-are enough videos with various spatial resolutions in it. For the
-motion feature extraction module, the resolution of the videos is
-resized to 224×224 for both the training and testing stages. We use
-PyTorch to implement the proposed models. The Adam optimizer
-with the initial learning rate 0.00001 and batch size 8 are used for
-training the proposed model on a server with NVIDIA V100. The
-hyper-parameter 𝜆is set as 1. For simplicity, we select the first
-frame in each chunk as the key frame. For the multi-scale quality
-fusion strategy, there are 𝑑=35,𝑛=1080 ,ℎ=11.3,𝐿=200, and
-𝑋2
-0=606, and the final multi-scale weights for UGC videos are
-𝑤1=0.8317,𝑤2=0.0939, and𝑤3=0.0745.
-4.1.3 Comparing Algorithms. We compare the proposed method
-with the following no-reference models:
-•IQA models: NIQE [ 24], BRISQUE [ 22], GM-LOG [ 41], VGG19
-[27], ResNet50 [7], and KonCept512 [9].
-•VQA models: V-BLIINDS [ 26], TLVQM [ 12], VIDEAL [ 33],
-RAPIQUE [34], VSFA [15], PVQ [44], and Li et al. [14].
-Since the number of videos in the LSVQ database is relatively
-large, we only compare some representative VQA models on the
-LSVQ database and omit the methods which perform poorly on the
-other four UGC databases.A Deep Learning based No-reference Quality Assessment Model for UGC Videos MM ’22, October 10–14, 2022, Lisboa, Portugal
-4.1.4 Evaluation Criteria. We adopt two criteria to evaluate the
-performance of VQA models, which are Pearson linear correlation
-coefficient (PLCC) and Spearman rank-order correlation coefficient
-(SRCC). PLCC reflects the prediction linearity of the VQA algorithm
-and SRCC indicates the prediction monotonicity. An excellent VQA
-model should obtain the value of SRCC and PLCC close to 1. Before
-calculating the PLCC, we follow the same procedure in [ 1] to map
-the objective score to the subject score using a four-parameter
-logistic function.
-For KoNViD-1k, YouTube-UGC, LBVD, and LIVE-YT-Gaming
-databases, we randomly split these databases into the training set
-with 80% videos and the test set with 20% videos for 10 times, and
-report the median values of SRCC and PLCC. For the LSVQ database,
-we follow the same training and test split suggested by [ 44] and
-report the performance on the test and test-1080p subsets.
-4.2 Performance Comparison with the SOTA
-Models
-The performance results of the VQA models on the KoNViD-1k,
-YouTube-UGC, LBVD, and LIVE-YT-Gaming databases are listed in
-Table 2, and on the LSVQ database are listed in Table 3. From Table
-2 and Table 3, we observe that the proposed model achieves the best
-performance on all five UGC VQA databases and leads by a large
-margin, which demonstrates that the proposed model does have a
-strong ability to measure the perceptual quality of various kinds of
-UGC videos. For the test-1080p subset of the LSVQ database, the
-proposed model is inferior to Li et al., which may be because the spa-
-tial resolution of most videos in the test-1080p subset is larger than
-1080p while the proposed model resizes the spatial resolution of test
-videos into 448×448, so the proposed model has a relatively poor
-ability to represent the characteristics of high-resolution videos.
-Through the multi-scale quality weighting fusion strategy, the pro-
-posed model can significantly improve the performance on the
-test-1080p subset.
-Then, most handcrafted feature based IQA models perform poorly
-on these UGC VQA databases especially for the LBVD and LIVE-
-YT-Gaming databases since they are designed for natural scene
-images with synthetic distortions and are difficult to handle the
-complex in-the-wild distortions and other video types such gaming,
-videoliving, etc. It is worth noting that through fine-tuning the deep
-CNN baseline i.e. ResNet50 on the VQA databases, it can achieve a
-pretty good performance, which also indicates that spatial features
-are very important for VQA tasks. For the NR VQA methods, the
-hand-crafted feature based NR VQA methods such as TLVQM and
-VIDEVAL achieve pretty well performance by incorporating the
-rich spatial and temporal quality features, such as NSS features,
-motion features, etc., but they are inferior to the deep learning
-based NR VQA methods due to the strong feature representation
-ability of CNN. VSFA extracts the spatial features from the pre-
-trained image recognition model, which are not quality-aware, and
-achieves relatively poor performance when compared with other
-deep learning based methods. PVQ and Li et al. methods both uti-
-lize the pretrained IQA model and ptretrained action recognition
-model to extract spatial and motion features respectively, and they
-perform better than other compared NR I/VQA methods but are
-inferior to the proposed model. Through training an end-to-endTable 4: The results of ablation studies on the LSVQ data-
-base. S and M means the spatial features and motion features
-respectively, and S∗means that the spatial features are ex-
-tracted by the pretrained image classification network.
-Database Test Test-1080p
-Criterion SRCC PLCC SRCC PLCC
-FeatureS∗+M 0.847 0.841 0.732 0.774
-S 0.827 0.829 0.702 0.757
-M 0.660 0.669 0.569 0.621
-RegressionGRU 0.858 0.855 0.735 0.788
-Transformer 0.860 0.861 0.753 0.799
-PoolingMethod in [15] 0.860 0.858 0.733 0.786
-1D CNN based 0.864 0.862 0.739 0.790
-Table 5: The SRCC results of cross-database evaluation. The
-model is trained on the LSVQ database.
-Database KoNViD-1k YouTube-UGC LBVD LIVE-YT-Gaming
-Pro. 0.860 0.789 0.689 0.642
-Pro. M.S. 0.859 0.822 0.711 0.683
-spatial feature extractor, the proposed model can take advantage of
-various video content and distortion types in the UGC databases
-and learn a better quality-aware feature representation. As a result,
-the proposed model achieves the best performance on all five UGC
-VQA databases.
-4.3 Ablation Studies
-In this section, we conduct several ablation studies to investigate
-the effectiveness of each module in the proposed model, including
-the feature extraction module, and the quality regression module.
-All the experiments are tested on the LSVQ database since it is the
-largest UGC VQA model and is more representative.
-4.3.1 Feature Extraction Module. The proposed model consists
-of the spatial feature extractor that learns the end-to-end spatial
-quality-aware features and the motion feature extractor that utilizes
-a pretrained action recognition model to represent motion informa-
-tion. Therefore, we first do not train the spatial feature extractor
-and directly use the weights trained on the ImageNet database to
-study the effect of the end-to-end training strategy for the spatial
-feature extractor. Then, we only use the end-to-end trained spatial
-features or the pretrained motion features to evaluate the quality of
-UGC videos to investigate the effect of these two kinds of features.
-The results are listed in Table 4. First, it is observed that the model
-using the motion features is inferior to the model using the spatial
-features and both of them are inferior to the proposed model, which
-indicates that both spatial and motion features are beneficial to the
-UGC VQA task and the spatial features are more important. Then,
-we find that end-to-end training for the spatial feature extractor can
-significantly improve the evaluation performance, which demon-
-strates that end-to-end trained spatial features represent better than
-that extracted by the pretrained image classification model.MM ’22, October 10–14, 2022, Lisboa, Portugal Wei Sun, Xiongkuo Min, Wei Lu, and Guangtao Zhai∗
-Table 6: Comparison of computational complexity for the six VQA models and two proposed models. Time: Second.
-Methods V-BLIINDS TLVQM VIDEVAL VSFA RAPIQUE Li et al. Pro. Pro. M.S.
-Time 61.982 219.992 561.408 56.424 38.126 61.971 6.929 8.448
-4.3.2 Quality Regression Module. In this paper, we use the MLP
-as the regression model to derive the chunk-level quality scores.
-However, in previous studies, some sequential models such as GRU
-[15], Transformer [ 14], etc. are also adopted to further consider the
-influence of the features extracted from adjacent frames. Here, we
-also adopt these methods as a comparison to investigate whether
-sequential models can improve the performance of the proposed
-models. Specifically, we replace the MLP module with the GRU and
-Transformer and keep other experimental setups the same. The
-results are listed in Table 4. We observe that models using GRU
-and Transformer are both inferior to the proposed model, which
-means that the MLP module is enough to regress the quality-aware
-features to quality scores though it is very simple. This conclusion
-is also consistent with [ 37]. The reason is that the proposed model
-and the model in [ 37] calculate the chunk-level quality score and
-the effect of adjacent frames are considered in the quality-aware
-features (i.e. motion features), while other VQA models [ 15] [14]
-calculate the frame-level quality scores, which may need to consider
-the effect of adjacent frames in the quality regression module.
-4.3.3 Quality Pooling Module. The proposed model uses the tem-
-poral average pooling method to fuse the chunk-level quality scores
-into the video level. It is noted that previous studies also propose
-several temporal pooling methods for VQA. In this section, we test
-two temporal pooling methods, which are the subjectively-inspired
-method introduced in [ 15] and a learning based temporal pooling
-method using the 1D CNN. The results are listed in Table 4. From
-Table 4, we observe that the average pooling strategy achieves sim-
-ilar performance to the learning based pooling method, and both of
-them are superior to the subjectively-inspired methods. Since the
-average pooling strategy is simpler and does not increase the extra
-parameters, we use the temporal average pooling method in this
-paper.
-4.4 Cross-Database Evaluation
-UGC videos may contain various kinds of distortions and content,
-most of which may not exist in the training set. Hence, the gen-
-eralization ability of the UGC VQA model is very important. In
-this section, we use the cross-database evaluation to test the gener-
-alization ability of the proposed model. Specifically, we train the
-proposed model on the LSVQ database and test the trained model
-on the other four UGC VQA databases. We list the results in Table
-5. It is observed that the proposed model achieves excellent per-
-formance in cross-database evaluation. The SRCC results on the
-KoNViD-1k and YouTube-UGC databases both exceed 0.8, which
-have surpassed most VQA models trained on the corresponding
-database. We find that the multi-scale quality fusion strategy can
-significantly improve the performance on the databases containing
-videos with different spatial resolutions (YouTube-UGC, LBVD, and
-LIVE-YT-Gaming), which further demonstrates its effectiveness.It is also observed that the performance on the LBVD and LIVE-
-YT-Gaming databases is not good as the other two databases. The
-reason is that the LBVD and LIVE-YT-Gaming databases contain
-live broadcasting and gaming videos respectively, which may rarely
-exist in the LSVQ database. Since the single database can not cover
-all kinds of video types and distortions, we may further improve the
-generalization ability of the proposed model via the multiple data-
-base training strategy [ 30] [51] or the continual learning manner
-[49] [50].
-4.5 Computational Complexity
-The computational complexity is a very important factor that needs
-to be considered in practical applications. Hence, we test the com-
-putational complexity in this section. All models are tested on a
-computer with i7-6920HQ CPU, 16G RAM, and NVIDIA Quadro
-P400. The deep learning based models and the handcrafted based
-models are tested using the GPU and CPU respectively. We report
-the running time for a video with the resolution of 1920 ×1080 and
-time duration of eight seconds in Table 6. It is seen that the proposed
-model has a considerably low running time compared with other
-VQA models. The reason is that we use very sparse frames to calcu-
-late the spatial features while other deep learning based methods
-need dense frames. Moreover, we extract the motion features at a
-very low resolution, which only adds little computational complex-
-ity to the proposed model. The very low computational complexity
-makes the proposed model suitable for practical applications.
-5 CONCLUSION
-In this paper, we propose an effective and efficient NR VQA model
-for UGC videos. The proposed model extracts the quality-aware
-features from the spatial domain and the spatial-temporal domain to
-measure the spatial distortions and motion distortions respectively.
-We train the spatial feature extractor in an end-to-end training
-manner, so the proposed model can make full use of the various
-spatial distortions and content in the current VQA database. Then,
-the quality-aware features are regressed into the quality scores
-by the MLP network, and the temporal average pooling is used
-to obtain the video-level quality scores. We further introduce the
-multi-scale quality fusion strategy to address the problem of quality
-assessment across different spatial resolutions. The experimental
-results show that the proposed model can effectively measure the
-quality of UGC videos.
-ACKNOWLEDGMENTS
-This work was supported by the National Natural Science Foun-
-dation of China (61831015, 61901260) and the National Key R&D
-Program of China 2021YFE0206700.A Deep Learning based No-reference Quality Assessment Model for UGC Videos MM ’22, October 10–14, 2022, Lisboa, Portugal
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txt/2205.05391.txt

@@ -1,362 +0,0 @@
-Query-Based Keyphrase Extraction from Long Documents
-Martin Docekal, Pavel Smrz
-Brno University of Technology
-idocekal@fit.vutbr.cz, smrz@fit.vutbr.cz
-Abstract
-Transformer-based architectures in natural language process-
-ing force input size limits that can be problematic when long
-documents need to be processed. This paper overcomes this
-issue for keyphrase extraction by chunking the long docu-
-ments while keeping a global context as a query defining the
-topic for which relevant keyphrases should be extracted. The
-developed system employs a pre-trained BERT model and
-adapts it to estimate the probability that a given text span
-forms a keyphrase. We experimented using various context
-sizes on two popular datasets, Inspec and SemEval, and a
-large novel dataset. The presented results show that a shorter
-context with a query overcomes a longer one without the
-query on long documents.1
-Introduction
-Keyphrase refers to a short language expression describ-
-ing the content of a longer text. Due to their concise form,
-keyphrases can be used for a quick familiarization with
-a document. They also improve the findability of docu-
-ments or passages within them. In the bibliographic records,
-keyphrase descriptors enable flexible indexing.
-Whether a text span is a keyphrase depends on the context
-of that span because a keyphrase for a specific topic may
-not be a keyphrase for another topic. The presented work
-builds on the idea that the topic can be explicitly given as
-an input to the keyphrase extraction algorithm in the form
-of a query. We approximate such a query with a document’s
-title in our experiments. We also investigate the influence of
-context size and document structure on the results.
-Related Work
-Traditional approaches to keyphrase extraction involve
-graph-based methods, such as TextRank (Mihalcea and Ta-
-rau 2004) and RAKE (Rose et al. 2010). Recently, many
-types of neural networks have been used for the task (Lin
-and Wang 2019; Sahrawat et al. 2020). Most of the deep
-learning work assumes the existence of a title and an abstract
-of the document and extracts keyphrases from them because
-Copyright © 2022 by the authors. All rights reserved.
-1The code is available at https://github.com/KNOT-FIT-
-BUT/QBEK.they struggle with longer inputs such as whole scientific ar-
-ticles (Kontoulis, Papagiannopoulou, and Tsoumakas 2021).
-Some works try to overcome this limitation by first creating
-a document summary and then extracting keyphrases from
-it (Kontoulis, Papagiannopoulou, and Tsoumakas 2021; Liu
-and Iwaihara 2021). Our research follows an alternative
-path, compensating for the limited context by a query spec-
-ifying a topic.
-Model
-First, a document is split into parts (contexts), which are fur-
-ther processed independently. Then, the devised model esti-
-mates the probability that a given continuous text span forms
-a keyphrase. It looks for boundaries tsandte, corresponding
-to the text span’s start and end, respectively. The inspiration
-for this approach comes from the task of reading compre-
-hension, where a similar technique is used to search for po-
-tential answers to a question in an input text (Devlin et al.
-2019). Formally, the model estimates probability:
-P(ts; tejx) =P(tsjx)P(tejx); (1)
-where xis the input sequence. It assumes that the start and
-the end of a text span < ts; te>are independent. The prob-
-abilities P(tsjx)andP(tejx)are obtained in the following
-way:
-P(tjx) =sigmoid (wT
-BERT (x)[] +b); (2)
-wherestands for the start and end positions. Weights
-wsandweare learnable vectors, bis the scalar bias and
-BERT (x)[i]is BERT (Devlin et al. 2019) vector representa-
-tion of a token from sequence xon position i. See the model
-illustration in Figure 1.
-The task of predicting whether a given token is the start
-token of a span or the end token could be seen as binary clas-
-sification with two classes start/end andnot start/not end ,
-respectively. The binary cross-entropy ( BCE ) loss function
-is used for training in the following way:
-BCE( vs; gs) + BCE( ve; ge); (3)
-where vsis a vector of probabilities that a token is the start
-token of a span, for each token in the input, and veis vec-
-tor computed analogously, but for the ends. The gsandge
-are vectors of ground truth probabilities of starts or ends,
-respectively.x = [CLS] title [SEP] context [SEP]
-P(t |x)s
-P(t |x)e
-BERT
-BERT
-Linear Layer
-SigmoidBERT(x)[i]Figure 1: Illustration of our model with query at the input.
-We work with two types of inputs. One consists of a text
-fragment such as a sentence, and the other uses a query (doc-
-ument title) and a text segment, separated by a special token.
-Various context sizes are explored in our experiments. The
-context size determines how big is the document part the
-model sees at once. Every context part of a document is pro-
-cessed independently. The final list of keyphrases is created
-by collecting keyphrase spans with their scores and selecting
-the top ones.
-Datasets
-Besides two standard datasets for keyphrase extraction, we
-created and used a novel dataset of long documents, referred
-to a Library, and we also prepared an unstructured version
-of the SemEval-2010 dataset. A comparison of the datasets
-is given in Table 1.
-dataset train val. testsentences
-(train)
-SemEval-2010 130 14 100 66 428 (72%)
-Unstructured-
-SemEval-2010130 14 100 45 346 (67%)
-Inspec 1 000 500 500 5 894 (25%)
-Library 48 879 499 499 298 217 589 (94%)
-Table 1: The number of documents in each split along with
-the total number of sentences in a train set. The percentage in
-the sentences column is the proportion of sentences without
-keyphrases.
-We had to annotate the spans that represent given
-keyphrases in the text as the discussed datasets provide just a
-list of associated keyphrases with no information about their
-actual positions. The search was case insensitive and the
-Porter stemmer was utilized for the SemEval and Hulth2003
-(Inspec) datasets. For the Library dataset, as it is in Czech,
-theMorphoDiTa lemmatizer2was used.
-SemEval-2010 (Kim et al. 2010) consists of whole
-plain texts from scientific articles. The dataset provides
-keyphrases provided by authors and readers. As it is
-common practice (Kim et al. 2010; Kontoulis, Papa-
-giannopoulou, and Tsoumakas 2021), we use a combina-
-tion of both in our experiments. Our validation dataset was
-2http://hdl.handle.net/11858/00-097C-0000-0023-43CD-0created by randomly choosing a subset of the train set. As
-the original data source does not explicitly provide the ti-
-tles, which we need to formulate a query, we have manually
-extracted the title of each document from the plain text.
-Documents in this dataset have a well-formed structure.
-They contain a title and abstract and are divided into sections
-introduced with a headline. As we want to investigate the
-influence of such structure on results, we have made an un-
-structured version of this dataset. We downloaded the orig-
-inal PDFs and used the GROBID3to get a structured XML
-version of them. We kept only the text from the document’s
-main body while the parts such as title, abstract, authors,
-or section headlines were removed. Nevertheless, document
-keyphrase annotations remain the same. We call this dataset
-Unstructured-SemEval-2010. The name SemEval is used to
-name these two collectively.
-Inspec (Hulth 2003) contains a set of title-abstract pairs
-collected from scientific articles. For each abstract, there are
-two sets of keyphrases — controlled , which are restricted to
-the Inspec thesaurus, and uncontrolled that can contain any
-suitable keyphrase. To be comparable with previous works
-(Hulth 2003; Liu and Iwaihara 2021), we used only the un-
-controlled set in our experiments.
-Library is a newly created dataset that takes advantage
-of a large body of scanned documents, provided by Czech
-libraries, that were converted to text by OCR software. This
-way of getting the text is unreliable, so the dataset contains
-many errors on the word and character level. The dataset
-builds on the documents where the language was recognized
-as ‘Czech’ by the OCR software.
-All used documents in the original data source are rep-
-resented by their significant content (the average number
-of characters per document is 529 276) and metadata. The
-metadata contains (not for all) keyphrases and document lan-
-guage annotations. We did not ask annotators to annotate
-each document. Instead, we selected metadata fields used by
-librarians as keyphrase annotations. So, our data and meta-
-data come from the real-world environment of Czech li-
-braries. We have filtered out all documents with less than
-five keyphrases.
-Documents come from more than 290 categories. Vari-
-ous topics such as mathematics, psychology, belles lettres,
-music, and computer science are covered. Not all anno-
-tated keyphrases can be extracted from the text. Consid-
-ering the lemmatization, the test set annotations contain
-about 53% of extractive keyphrases. Bibliographic field Title
-(MARC 2454) is used as the query. Note that the field may
-contain additional information to the title, such as authors.
-Experimental Setup
-The implemented system builds on PyTorch5and PyTorch
-Lightning6tools. The BERT part of the model uses the
-3https://github.com/kermitt2/grobid
-4https://www.loc.gov/marc/bibliographic/bd245.html
-5https://pytorch.org/
-6https://www.pytorchlightning.ai/1359 19 370:120:140:160:180:2
-input size [sentences]F1@10sentences only sentences + query
-sentences only (unst) sentences + query (unst)
-Figure 2: Results for SemEval-2010 and Unstructured-
-SemEval-2010 test set. The light red and blue areas are con-
-fidence intervals with a confidence level of 0.95. Each point
-corresponds to an average of five runs.
-implementation of BERT by Hugging Face7and it is ini-
-tialized with pre-trained weights of bert-base-multilingual-
-cased . These weights are also optimized during fine-tuning.
-The Adam optimiser with weight decay (Loshchilov and
-Hutter 2017) is used in all the experiments. The evaluation
-during training on the validation set is done every 4 000 op-
-timization steps for the Library dataset and every 50 steps
-for Inspec (25 for whole abstracts with titles). For SemEval
-datasets, the number of steps differs among experiments.
-Early stopping with patience 3 is applied, so the training
-ends when the model stops improving. Batch size 128 is
-used for experiments with the Library dataset, and batch
-size 32 is used for Inspec and SemEval datasets. The learn-
-ing rate 1E-06 is used for the experiments with SemEval
-datasets, while it is set to the value of 1E-05 for all other
-datasets. Inputs longer than a maximum input size are split
-into sequences of roughly the same size in a way that for-
-bids splitting of keyphrase spans. In edge cases (split is not
-possible), the input is truncated. No predicted span is longer
-than 6 tokens.
-The official script for SemEval-2010 is used for evalua-
-tion. However, the normalization of keyphrases is different
-for the Library dataset as we have used the mentioned Czech
-lemmatizer instead of the original stemmer. We use the F1
-over the top five (F1@5) candidates for the Library dataset
-and over the top ten (F1@10) for the rest.
-Experiments
-The performed experiments investigate the influence of
-queries on four different datasets, the output quality with
-various context sizes, and the impact of the document struc-
-ture.
-The first set of experiments is performed on long docu-
-ments with a well-formed structure from the SemEval-2010
-7https://huggingface.co/1359 19 370255075
-input size [sentences]too long inputs [%]sentences only sentences + query
-Figure 3: The proportion of inputs longer than the maximum
-input size for SemEval-2010 train set.
-1359 19 370255075
-input size [sentences]proportion [%]
-Figure 4: The proportion of contexts containing at least one
-section headline as a substring for SemEval-2010 test set.
-dataset and compares them with SemEval’s unstructured
-version. Figure 2 shows that inputs with a query are bet-
-ter than those without a query, but the last point. For the
-structured input, it can be seen that from the point with 19
-sentences, the performance of input with query stops with
-the fast growth. It correlates with Figure 3 showing the sat-
-uration of the model input. Notice that from 19 sentences,
-the input becomes more saturated, and the splitting strategy
-starts shrinking contexts.
-It is not surprising that the nominal values are lower for
-unstructured inputs. On the other hand, it is clear that the
-query has a bigger influence on the unstructured version, es-
-pecially for short context sizes, because the average abso-
-lute difference among results (with- and without a query) for
-each context size is 2.2% compared to 1.29% for the struc-
-tured one.
-Looking at the curve of results with a query on an un-
-structured version, we assume that the model can exploit a
-document structure without explicitly tagging it with special
-tokens because additional context size above the three sen-
-tences is not much beneficial compared to the case with the
-document structure. This hypothesis is supported by the fact
-that the proportion of the context containing structured infor-
-mation grows with context size, as is demonstrated in Figure
-4 showing the proportion of contexts containing a section
-headline.
-The second set of experiments was performed on our Li-
-brary dataset. The results can be seen in Figure 5. We have
-chosen F1@5 because only approximately half of the doc-
-uments have ten and more keyphrases. Again, the results
-show that queries are beneficial. Also, it can be seen that
-the shape of the query curve is similar to Unstructured-
-SemEval-2010. The average absolute difference between the
-version with and without query is now 3.1%. For F1@10, it1359 19 370:10:15
-input size [sentences]F1@5sentences only sentences + query
-Figure 5: Results for Library test set for various context
-sizes. The light red area symbolizes a confidence interval
-with a confidence level of 0.95. Each point is average from
-three runs.
-Inspec SemEval 2010
-F1@10 F1@10
-TextRank 15.28 6.55
-KFBS + BK-Rank 46.62 15.59
-DistilRoBERTa + TF-IDF - 16.2
-context
-[sentences]query
-whole document 7 39.67 -
-17 40.26 14.96
-3 39.95 16.06
-197 - 17.18
-3 - 18.56
-Table 2: Comparison of achieved results with other work.
-KFBS + BK-Rank and TextRank is from (Liu and Iwaihara
-2021). The DistilRoBERTa + TF-IDF is from (Kontoulis,
-Papagiannopoulou, and Tsoumakas 2021). Our results are
-averages from five runs.
-is 2.3, which is close to the value for the unstructured version
-of SemEval.
-The last set of experiments is done on the Inspec dataset,
-which has only titles and abstracts. The purpose is to in-
-vestigate the influence of a query on short inputs containing
-mainly salient sentences. Results are summarized in Table 2,
-which also compares our results with other works. It shows
-that the results for a single sentence, a single sentence with
-a title, and whole abstract with a title are similar. The ex-
-planation can be that the abstract contains mainly salient
-sentences containing keyphrases, and also, the abstract it-
-self defines the topic of the article. A similar observation
-is presented in (Liu and Iwaihara 2021), where the version
-without summarization gives similar results as the extraction
-performed on a summary.
-Conclusions
-We have conducted experiments that show that query-based
-keyphrase extraction is promising for processing long doc-
-uments. Our experiments show the relationship between
-the context size and the performance of the BERT-basedkeyphrase extractor. The developed model was evaluated on
-four datasets; one of them is non-English. The datasets al-
-lowed us to find when the query-based approach is benefi-
-cial and when not. It was shown that a query gives no bene-
-fit when extracting keyphrases from abstracts. On the other
-hand, it is beneficial for long documents, particularly those
-without a well-formed document structure on short context
-sizes.
-Acknowledgment
-This work was supported by the Technology Agency of the
-Czech Republic, Grant FW03010656 – MASAPI: Multilin-
-gual assistant for searching, analysing and processing infor-
-mation and decision support. The computation used the in-
-frastructure supported by the Ministry of Education, Youth
-and Sports of the Czech Republic through the e-INFRA CZ
-(ID:90140).
-References
-Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2019.
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txt/2205.08365.txt

@@ -1,823 +0,0 @@
-Deep Supervised Information Bottleneck Hashing for Cross-modal Retrieval
-based Computer-aided Diagnosis
-Yufeng Shi1, Shuhuang Chen1, Xinge You1*, Qinmu Peng1, Weihua Ou2, Yue Zhao3
-1Huazhong University of Science and Technology
-2Guizhou Normal University
-3Hubei University
-fyufengshi17, shuhuangchen, youxg, pengqinmu [email protected], [email protected], [email protected]
-Abstract
-Mapping X-ray images, radiology reports, and other medi-
-cal data as binary codes in the common space, which can
-assist clinicians to retrieve pathology-related data from het-
-erogeneous modalities (i.e., hashing-based cross-modal med-
-ical data retrieval), provides a new view to promot computer-
-aided diagnosis. Nevertheless, there remains a barrier to boost
-medical retrieval accuracy: how to reveal the ambiguous se-
-mantics of medical data without the distraction of superflu-
-ous information. To circumvent this drawback, we propose
-Deep Supervised Information Bottleneck Hashing (DSIBH),
-which effectively strengthens the discriminability of hash
-codes. Specifically, the Deep Deterministic Information Bot-
-tleneck (Yu, Yu, and Pr ´ıncipe 2021) for single modality is
-extended to the cross-modal scenario. Benefiting from this,
-the superfluous information is reduced, which facilitates the
-discriminability of hash codes. Experimental results demon-
-strate the superior accuracy of the proposed DSIBH com-
-pared with state-of-the-arts in cross-modal medical data re-
-trieval tasks.
-The rapid development of medical technology not only
-provides diverse medical examinations but also produces
-tremendous amounts of medical data, ranging from X-ray
-images to radiology reports. It is an experience-demanding,
-time-consuming, and error-prone job to manually assess
-medical data and diagnose disease. To reduce the work
-burden of physicians and optimize the diagnostic process,
-computer-aided diagnosis (CAD) systems including classi-
-fier based CAD(Shi et al. 2020; In ´es et al. 2021) and content-
-based image retrieval (CBIR) based CAD(Yang et al. 2020;
-Fang, Fu, and Liu 2021) have been designed to automatically
-identify illness. Although the two types of methods greatly
-promote the development of CAD, existing systems ignore
-the character of current medical data, which is diverse in
-modality and huge in terms of scale. Therefore, we introduce
-cross-modal retrieval (CMR) (Wang et al. 2016) techniques
-and construct a CMR-based CAD method using semantic
-hashing (Wang et al. 2017) to handle the above challenges.
-With the help of CMR that projects multimodal data into
-the common space, samples from different modalities can be
-directly matched without the interference of heterogeneity.
-*Contact Author
-Accepted by the AAAI-22 Workshop on Information Theory for
-Deep Learning (IT4DL).Therefore, CMR-based CAD can not only retrieve the se-
-mantically similar clinical profiles in heterogeneous modal-
-ities but also provide diagnosis results according to the pre-
-vious medical advice. Compared with the classifier-based
-CAD that only provides diagnosis results, CMR-based CAD
-is more acceptable due to the interpretability brought by
-retrieved profiles. Compared with the CBIR-based CAD,
-CMR-based CAD wins on its extended sight of multi-modal
-data, which meets the requirement of current medical data.
-Recently, extensive work on hashing-based CMR that
-maps data from different modalities into the same hamming
-space, has been done by researchers to achieve CMR (Li
-et al. 2018; Zhu et al. 2020; Yu et al. 2021). Due to its com-
-pact binary codes and XOR distance calculation, hashing-
-based CMR possesses low memory usage and high query
-speed (Wang et al. 2017), which is also compatible with
-the huge volume of current medical data. In terms of ac-
-curacy, the suitable hashing-based solutions for CMR-based
-CAD are deep supervised hashing (DSH) methods (Xie et al.
-2020; Zhan et al. 2020; Yao et al. 2021). With the guid-
-ance of manual annotations, deep supervised methods usu-
-ally perform hash code learning based on the original data
-via neural networks. Inspired by the information bottle-
-neck principle (Tishby, Pereira, and Bialek 1999), the above-
-mentioned optimization procedure can be viewed as build-
-ing hash code Gabout a semantic label Ythrough samples
-in different modalities X=/braceleftbig
-X1,X2/bracerightbig
-, which can be for-
-mulated as:
-maxL=I(G;Y)−βI(G;X), (1)
-whereI(·;·)represents the mutual information, and βis a
-hyper-parameter. As quantified by I(G;Y), current DSH
-methods model the semantic annotations to establish pair-
-wise, triplet-wise or class-wise relations, and maximize the
-correlation between hash codes and the semantic relations.
-Despite the consideration of semantic relations, the neglect
-ofI(G;X)will result in the retention of redundant infor-
-mation in the original data, thus limiting the improvement
-of the retrieval accuracy. I(G;X)measures the correlation
-between the hash code Gand the data from two modalities
-X, which can be used to reduce the superfluous informa-
-tion from medical data, and constrain the hash code to grasp
-the correct semantics from annotations. Therefore, it can be
-expected that the optimization of Eq. (1) can strengthen thearXiv:2205.08365v1  [cs.LG]  6 May 2022discriminability of hash codes, which improves the accuracy
-of CMR-based CAD.
-To perform CMR-based CAD, we design a novel method
-named Deep Supervised Information Bottleneck Hash-
-ing (DSIBH), which optimizes the information bottleneck
-to strengthen the discriminability of hash codes. Specifi-
-cally, to avoid variational inference and distribution assump-
-tion, we extend the Deep Deterministic Information Bot-
-tleneck (DDIB) (Yu, Yu, and Pr ´ıncipe 2021) from single
-modality to the cross-modal scenario for hash code learning.
-To summarize, our main contributions are fourfold:
-• The cross-modal retrieval technique based on semantic
-hashing is introduced to establish computer-aided diag-
-nosis systems, which is suitable for the current large-
-scale multi-modal medical data.
-• A deep hashing method named DSIBH, which optimizes
-the hash code learning procedure following the informa-
-tion bottleneck principle to reduce the distraction of su-
-perfluous information, is proposed for CMR-based CAD.
-• To reduce the adverse impact of variational inference
-or distribution assumption, the Deep Deterministic In-
-formation Bottleneck is elegantly extended to the cross-
-modal scenario for hash code learning.
-• Experiments on the large-scale multi-modal medical
-dataset MIMIC-CXR show that DSIBH can strengthen
-the discriminability of hash codes more effectively than
-other methods, thus boosting the retrieval accuracy.
-Related Work
-In this section, representative CAD approaches and hashing-
-based solutions of cross-modal retrieval are briefly reviewed.
-To make readers easier to understand our work, some knowl-
-edge of the DDIB is also introduced.
-Computer-aided Diagnosis
-CAD approaches generally fall into two types including
-classifier-based CAD and CBIR-based CAD. Thanks to the
-rapid progress of deep learning, classifier-based CAD meth-
-ods (Zhang et al. 2019; de La Torre, Valls, and Puig 2020)
-can construct task-specific neural networks to categorize
-histopathology images and employ the outcomes as the diag-
-nosis. On the other side, CBIR-based CAD can provide clin-
-ical evidence since they retrieve and visualize images with
-the most similar morphological profiles. According to the
-data type of representations, existing CBIR methods can be
-divided into continuous value CBIR (Erfankhah et al. 2019;
-Zhen et al. 2020) and hashing-based CBIR (Hu et al. 2020;
-Yang et al. 2020). In the age of big data, the latter increas-
-ingly become mainstream due to the low memory usage and
-high query speed brought by hashing. Although substantial
-efforts have been made to analyse clinical image, medical
-data such as radiology reports in other modalities are ig-
-nored. Consequently, CAD is restricted in single modality
-and the cross-modal relevance between different modalities
-still waits to be explored.Cross-modal Retrieval
-Cross-modal hashing has made remarkable progress in han-
-dling cross-modal retrieval, and this kind of methods can
-be roughly divided into two major types including unsuper-
-vised approaches and supervised approaches in terms of the
-consideration of semantic information. Due to the absence of
-semantic information, the former usually relies on data dis-
-tributions to align semantic similarities of different modali-
-ties (Liu et al. 2020; Yu et al. 2021). For example, Collec-
-tive Matrix Factorization Hashing (Ding et al. 2016) learns
-unified hash codes by collective matrix factorization with
-a latent factor model to capture instance-level correlations.
-Recently, Deep Graph-neighbor Coherence Preserving Net-
-work (Yu et al. 2021) extra explores graph-neighbor coher-
-ence to describe the complex data relationships. Although
-data distributions indeed help to solve cross-modal retrieval
-to some extent, one should note that unsupervised methods
-fail to manage the high-level semantic relations due to the
-neglect of manual annotations.
-Supervised hashing methods are thereafter proposed to
-perform hash code learning with the guidance of manual
-annotations. Data points are encoded to express semantic
-similarity such as pair-wise(Shen et al. 2017; Wang et al.
-2019), triplet-wise (Hu et al. 2019; Song et al. 2021) or
-multi-wise similarity relations(Cao et al. 2017; Li et al.
-2018). As an early attempt with deep learning, Deep Cross-
-modal Hashing (Jiang and Li 2017) directly encodes origi-
-nal data points by minimizing the negative log likelihood of
-the cross-modal similarities. To discover high-level semantic
-information, Self-Supervised Adversarial Hashing (Li et al.
-2018) harnesses a self-supervised semantic network to pre-
-serve the pair-wise relationships. Although various relations
-have been built between the hash code and the semantic la-
-bels, the aforementioned algorithms still suffer from the dis-
-traction of superfluous information, which is caused by the
-connections between the hash code and the original data,
-Consequently, for CMR-based CAD, there remains a need
-for a deep hashing method which can reduce the superfluous
-information to strengthen the discriminability of hash codes.
-Deep Deterministic Information Bottleneck
-Despite great efforts to handle the ambiguous semantics of
-medical data, the discriminability of hash codes still needs
-to be strengthened. To alleviate such limitation, a promising
-solution is Deep Deterministic Information Bottleneck (Yu
-et al. 2021) that has been proved to reduce the superfluous
-information during feature extraction. Before elaborating on
-our solution, we introduce basic knowledge on DDIB below.
-DDIB intends to adopt a neural network to parameterize
-information bottleneck (Tishby, Pereira, and Bialek 1999),
-which considers extracting information about a target signal
-Ythrough a correlated observable X. The extracted infor-
-mation is represented as a variable T. The information ex-
-traction process can be formulated as:
-maxLIB=I(T;Y)−βI(T;X). (2)
-When the above objective is optimized with a neural net-
-work,Tis the output of one hidden layer. To update theparameters of networks, the second item in Eq. (2) is cal-
-culated with the differentiable matrix-based R ´enyi’sα-order
-mutual information:
-Iα(X;T) =Hα(X) +Hα(T)−Hα(X,T),(3)
-whereHα(·)indicates the matrix-based analogue to R ´enyi’s
-α-entropy and Hα(·,·)is the matrix-based analogue to
-R´enyi’sα-order joint-entropy. More details of the matrix-
-based R ´enyi’sα-order entropy functional can be found in
-(Yu et al. 2019).
-For the first item in Eq. (2), since I(T;Y) =H(Y)−
-H(Y|T), the maximization of I(T;Y)is converted to
-the minimization of H(Y|T). Given the training set
-{xi,yi}N
-i=1, the average cross-entropy loss is adopted to
-minimize the H(Y|T):
-1
-NN/summationdisplay
-i=1Et∼p(t|xi)[−logp(yi|t)], (4)
-Therefore, DDIB indicates that the optimization of Infor-
-mation Bottleneck in single modality can be achieved with
-a cross-entropy loss and a differentiable mutual informa-
-tion itemI(T;X). Obviously, the differentiable optimiza-
-tion strategy of information bottleneck in DDIB can benefit
-DSH methods in terms of superfluous information reduction.
-Method
-In this section, we first present the problem definition, and
-then detail our DSIBH method. The optimization is finally
-given. For illustration purposes, our DSIBH is applied in X-
-ray images and radiology reports.
-Notation and problem definition
-Matrix and vector used in this paper are represented by bold-
-face uppercase letter (e.g., G) and boldface lowercase let-
-ter (e.g., g) respectively./bardbl·/bardbldenotes the 2-norm of vectors.
-sign(·)is defined as the sign function, which outputs 1 if its
-input is positive else outputs -1.
-LetX1=/braceleftbig
-x1
-i/bracerightbigN
-i=1andX2=/braceleftbig
-x2
-j/bracerightbigN
-j=1symbolize X-
-ray images and radiology reports in the training set, where
-x1
-i∈Rd1,x2
-j∈Rd2. Their semantic labels that indicate
-the existence of pathology are represented by Y={yl}N
-l=1,
-where yl={yl1,yl2,...,yld3}∈Rd3. Following (Cao et al.
-2016; Jiang and Li 2017; Li et al. 2018), we define the se-
-mantic affinities SN×Nbetween x1
-iandx2
-jusing semantic
-labels. If x1
-iandx2
-jshare at least one category label, they
-are semantically similar and Sij= 1. Otherwise, they are
-semantically dissimilar and thus Sij= 0.
-The goal of the proposed DSIBH is to learn hash functions
-f1/parenleftbig
-θ1;X1/parenrightbig
-:Rd1→Rdcandf2/parenleftbig
-θ2;X2/parenrightbig
-:Rd2→Rdc,
-which can map X-ray images and radiology reports as ap-
-proximate binary codes G1andG2in the same continuous
-space respectively. Later, binary codes can be generated by
-applying a sign function to G1,2.
-Meanwhile, hamming distance D/parenleftbig
-g1
-i,g2
-j/parenrightbig
-between hash
-codes g1
-iandg2
-jneeds to indicate the semantic similaritySijbetween x1
-iandx2
-j, which can be formulated as:
-Sij∝−D/parenleftbig
-g1
-i,g2
-j/parenrightbig
-. (5)
-Information Bottleneck in Cross-modal Scenario
-To improve the accuracy of CMR-based CAD, the super-
-fluous information from the medical data in the hash code
-learning procedure should be reduced via the information
-bottleneck principle. Therefore, the information bottleneck
-principle in single modality should be extended to the cross-
-modal scenario, where one instance can own descriptions in
-different modalities.
-Analysis starts from the hash code learning processes for
-X-ray images and radiology reports respectively. Follow-
-ing the information bottleneck principle, the basic objective
-functions can be formulated as:
-maxLIB1=I/parenleftbig
-G1;Y1/parenrightbig
-−βI/parenleftbig
-G1;X1/parenrightbig
-,
-maxLIB2=I/parenleftbig
-G2;Y2/parenrightbig
-−βI/parenleftbig
-G2;X2/parenrightbig
-. (6)
-In cross-modal scenario, X-ray images and radiology re-
-ports in the training set are collected to describe the com-
-mon pathology. Therefore, the image-report pairs own the
-same semantic label. To implement this idea, Eq. (6) is trans-
-formed to:
-maxLIB1=I/parenleftbig
-G1;Y/parenrightbig
-−βI/parenleftbig
-G1;X1/parenrightbig
-,
-maxLIB2=I/parenleftbig
-G2;Y/parenrightbig
-−βI/parenleftbig
-G2;X2/parenrightbig
-, (7)
-Furthermore, the same hash code should be assigned for the
-paired samples to guarantee the consistency among different
-modalities, which is achieved with the /lscript2loss:
-minLCONS =E/bracketleftBig/vextenddouble/vextenddoubleg1−gy/vextenddouble/vextenddouble2/bracketrightBig
-+E/bracketleftBig/vextenddouble/vextenddoubleg2−gy/vextenddouble/vextenddouble2/bracketrightBig
-,(8)
-whereGyrepresents the modality-invariant hash codes for
-the image-report pairs.
-Incorporating Eq. (7) and Eq. (8), the overall objective of
-the information bottleneck principle in cross-modal scenario
-is formulated as:
-maxLIBC=/parenleftbig
-I/parenleftbig
-G1;Y/parenrightbig
-+I/parenleftbig
-G2;Y/parenrightbig/parenrightbig
-(9)
-−β/parenleftbig
-I/parenleftbig
-G1;X1/parenrightbig
-+I/parenleftbig
-G2;X2/parenrightbig/parenrightbig
-−γ/parenleftBig
-E/bracketleftBig/vextenddouble/vextenddoubleg1−gy/vextenddouble/vextenddouble2/bracketrightBig
-+E/bracketleftBig/vextenddouble/vextenddoubleg2−gy/vextenddouble/vextenddouble2/bracketrightBig/parenrightBig
-.
-Deep Supervised Information Bottleneck Hashing
-Following the information bottleneck principle in cross-
-modal scenario (i.e., Eq. (9)), three variables including
-Gy,G1andG2should be optimized. To obtain modality-
-invariantGy, we build labNet fyto directly transform se-
-mantic labels into the pair-level hash codes. The labNet is
-formed by a two-layer Multi-Layer Perception (MLP) whose
-nodes are 4096 and c. Then, we build imgNet f1and txtNet
-f2as hash functions to generate hash codes G1andG2. For
-X-ray images, we modify CNN-F (Chatfield et al. 2014)
-to build imgNet with the consideration of network scale.
-To obtaincbit length hash codes, the last fully-connectedlayer in the origin CNN-F is changed to a c-node fully-
-connected layer. For radiology reports, we first use the multi-
-scale network in (Li et al. 2018) to extract multi-scale fea-
-tures and a two-layer MLP whose nodes are 4096 and cto
-transform them into hash codes. Except the activation func-
-tion of last layers is tanh to approximate the sign (·)func-
-tion, other layers use ReLU as activation functions. To im-
-prove generalization performance, Local Response Normal-
-ization (LRN) (Krizhevsky, Sutskever, and Hinton 2012) is
-applied between layers of all MLPs. One should note that the
-application of CNN-F (Chatfield et al. 2014) and multi-scale
-network (Li et al. 2018) is only for illustrative purposes; any
-other networks can be integrated into our DSIBH as back-
-bones of imgNet and txtNet.
-As described before, semantic labels are encoded as hash
-codesGy. To preserve semantic similarity, the loss function
-of labNet is:
-min
-Gy,θyLy=Ly
-1+ηLy
-2 (10)
-=−N/summationdisplay
-l,j/parenleftbig
-Slj∆lj−log/parenleftbig
-1 +e∆lj/parenrightbig/parenrightbig
-+ηN/summationdisplay
-l=1/parenleftBig
-/bardblgy
-l−fy(θy;yl)/bardbl2/parenrightBig
-,
-s.t.Gy={gy
-l}N
-l=1∈{− 1,1}c
-where∆lj=fy(θy;yl)Tfy/parenleftbig
-θy;yj/parenrightbig
-,fy(θy;yl)is the
-output of labNet for yl,gy
-lis the hash codes of fy(θy;yl)
-handled bysign (·), andηaims to adjust the weight of loss
-items.
-The first term of Eq. (10) intends to minimize the nega-
-tive log likelihood of semantic similarity with the likelihood
-function, which is defined as follows:
-p/parenleftbig
-Slj|fy(θy;yl),fy/parenleftbig
-θy;yj/parenrightbig/parenrightbig
-=/braceleftbiggσ(∆lj) Slj= 1
-1−σ(∆lj)Slj= 0,
-(11)
-whereσ(∆lj) =1
-1+e−∆ljis the sigmoid function. Mean-
-while, the second term restricts the outputs of labNet to ap-
-proximate binary as the request of hash codes.
-After the optimization of labNet, the modality-invariant
-hash codeGyis obtained. The next step is to optimize the
-imgNet and txtNet to generate G1andG2respectively fol-
-lowing Eq. (9). For the first item in Eq. (9), DDIB interprets
-it as a cross-entropy loss (i.e., Eq. (4)). In our implement, Gy
-is also used as class-level weight in the cross-entropy loss,
-which intends to make G1andG2inherent the semantic sim-
-ilarity of the modality-invariant hash code. Specifically, the
-non-redundant multi-label annotations are transformed into
-Ny-class annotations{¯yl}Ny
-l=1, and their corresponding hash
-codes are regarded as the class-level weights. The weightedcross-entropy loss is formulated as:
-min
-θmLm
-1=−1
-NN/summationdisplay
-iNy/summationdisplay
-l¯yllog (ail), (12)
-ail,exp/parenleftBig
-(¯gy
-l)Tgm
-i/parenrightBig
-/summationtextNy
-l/primeexp/parenleftBig
-(¯gy
-l/prime)Tgm
-i/parenrightBig,
-wheremindicates the modality 1 or 2.
-For the second item in Eq. (9), we adopt the differen-
-tiable matrix-based R ´enyi’sα-order mutual information to
-estimate:
-min
-θmLm
-2=I(Gm;Xm). (13)
-For the third item in Eq. (9), the /lscript2loss is directly used:
-min
-θmLm
-3=N/summationdisplay
-i=1/parenleftBig
-/bardblgy
-i−gm
-i/bardbl2/parenrightBig
-. (14)
-By merging Eqs. (12), (13) and (14) together, we obtain
-the loss function of imgNet (or txtNet), formulated as the
-following minimization problem:
-min
-θmLm=Lm
-1+βLm
-2+γLm
-3, (15)
-whereβandγare hyper-parameters that are used to adjust
-the weights of loss items.
-Optimization
-The optimization of our DSIBH includes two parts: learning
-the modality-invariant hash code Gyand learning the hash
-codes G1andG2for X-ray images and radiology reports
-respectively. Learning Gyequals to optimize θy+. For hash
-codes of modality m,θmneeds to be optimized. The whole
-optimization procedure is summarized in Algorithm 1.
-Forθyof labNet, Eq. (10) is derivable. Therefore, Back-
-propagation algorithm (BP) with mini-batch stochastic gra-
-dient descent (mini-batch SGD) method is applied to update
-it. As for gy
-l, we use Eq. (16) to update:
-gy
-l=sign (fy(θy;yl)). (16)
-For imgNet and txtNet, we also use the BP with mini-
-batch SGD method to update θ1andθ2.
-Once Algorithm 1 converges, the well-trained imgNet and
-txtNet with sign(·)are used to handle out-of-sample data
-points from modality m:
-gm
-i=sign (fm(θm;xm
-i)). (17)
-Experiments
-In this section, we first introduce the dataset used for assess-
-ment and specify the experimental setting. Following this,
-we demonstrate that the proposed DSIBH can achieve the
-state-of-the-art performance on CMR-based CAD.Table 1: Comparison with baselines in terms of MAP on CMR-based CAD. The best results are marked with bold .
-MethodX→R R →X
-16 bits 32 bits 64 bits 128 bits 16 bits 32 bits 64 bits 128 bits
-CCA (Hotelling 1992) 0.3468 0.3354 0.3273 0.3215 0.3483 0.3368 0.3288 0.3230
-CMSSH (Bronstein et al. 2010) 0.4224 0.4020 0.3935 0.3896 0.3899 0.3967 0.3646 0.3643
-SCM (Zhang and Li 2014) 0.4581 0.4648 0.4675 0.4684 0.4516 0.4574 0.4604 0.4611
-STMH (Wang et al. 2015) 0.3623 0.3927 0.4211 0.4387 0.3980 0.4183 0.4392 0.4453
-CMFH (Ding et al. 2016) 0.3649 0.3673 0.3736 0.3760 0.4130 0.4156 0.4303 0.4309
-SePH (Lin et al. 2016) 0.4684 0.4776 0.4844 0.4903 0.4475 0.4555 0.4601 0.4658
-DCMH (Jiang and Li 2017) 0.4834 0.4878 0.4885 0.4839 0.4366 0.4513 0.4561 0.4830
-SSAH (Li et al. 2018) 0.4894 0.4999 0.4787 0.4624 0.4688 0.4806 0.4832 0.4833
-EGDH (Shi et al. 2019) 0.4821 0.5010 0.4996 0.5096 0.4821 0.4943 0.4982 0.5041
-DSIBH 0.5001 0.5018 0.5116 0.5172 0.4898 0.4994 0.4997 0.5084
-Query X-ray images Retrieved  radiology reports via our DSIBH
-Query radiology reports Retrieved X -ray images via our DSIBH(a)
-(b)Theendotracheal tube tipis6cmabove
-the carina .Nasogastric tube tipis
-beyond theGEjunction andofftheedge
-ofthefilm.Aleftcentral lineispresent in
-thetipisinthemidSVC.Apacemaker is
-noted ontheright inthelead projects
-over the right ventricle .There is
-probable scarring inboth lung apices .
-There arenonew areas ofconsolidation .
-There isupper zone redistribution and
-cardiomegaly suggesting pulmonary
-venous hypertension .There isno
-pneumothorax .
-There iscardiomegaly .Apacemaker is
-present with thelead overlying theright
-ventricle .Anapparent Swan -Ganz
-catheter ispresent, with tipoverlying the
-distal right pulmonary artery .There is
-upper zone re-distribution, with mild
-vascular plethora, butnoovert CHF.No
-focal infiltrate isdetected .Possible trace
-fluid attheleftcostophrenic angle .Ascompared totheprevious radiograph,
-thepatient hasreceived aright -sided
-chest tube .The tube isincorrect
-position, the right lung isnow fully
-expanded, there isnoevidence ofright
-pneumothorax .Incomparison with thestudy of___,the
-patient has taken abetter inspiration .
-Continued enlargement ofthecardiac
-silhouette with minimal central vascular
-congestion .Right PICC lineisstable .No
-evidence ofacute focal pneumonia .
-Since prior radiograph from ___, the
-mediastinal drain tube hasbeen removed .
-There isnopneumothorax .Both lung
-volumes arevery low.Bilateral, right side
-more than left side, moderate
-pulmonary edema has improved .
-Widened cardiomediastinal silhouette is
-more than itwas on___;however, this
-appearance could beexacerbation from
-lowlung volumes .Patient isstatus post
-median sternotomy with intact sternal
-sutures .Atelectasis; Pleural Effusion; 
-PneumothoraxAtelectasis; Pleural Effusion; Support 
-DevicesAtelectasis; CardiomegalyAtelectasis; Cardiomegaly; Pleural 
-Effusion; Support DevicesAtelectasis; Cardiomegaly
-Edema; Enlarged Cardiomediastinum ; 
-Support DevicesEdema; Lung Opacity; Pleural Effusion; 
-Pneumonia; Support DevicesCardiomegaly; Pleural Effusion; 
-Support DevicesAtelectasis; Pleural Effusion; Support 
-DevicesCardiomegaly; Edema; Pleural 
-Effusion; Support Devices
-Figure 1: The top 4 profiles retrieved by our DSIBH on the MIMIC-CXR dataset with 128 bits.
-Experimental setting
-The large-scale chest X-ray and radiology report dataset
-MIMIC-CXR (Johnson et al. 2019) is used to evaluate the
-performance of DSIBH. Some statistics of this dataset are
-introduced as follows.
-MIMIC-CXR1consists of chest X-ray images and radi-
-ology reports sourced from the Beth Israel Deaconess Med-
-ical Center between 2011-2016. Each radiology report is as-
-sociated with at least one X-ray image and annotated with
-a 14-dimensional label indicating the existence of pathol-
-ogy or lack of pathology. To evaluate the performance of
-CMR-based CAD, we adopt 73876 image-report pairs for
-assessment. During the comparison process, radiology re-
-ports are represented as bag-of-word vectors according to
-the top 617 most-frequent words. In the testing phase, we
-randomly sample 762 image-report pairs as query set and
-regard the rest as retrieval set. In the training phase, 14000
-pairs from the retrieval set are used as training set.
-The proposed DSIBH is compared with nine state-of-
-the-arts in hashing-based CMR including CCA (Hotelling
-1992), CMSSH (Bronstein et al. 2010), SCM (Zhang and
-Li 2014), STMH (Wang et al. 2015), CMFH (Ding et al.
-1https://physionet.org/content/mimic-cxr/2.0.0/2016), SePH (Lin et al. 2016), DCMH (Jiang and Li 2017),
-SSAH (Li et al. 2018), and EGDH (Shi et al. 2019). CCA,
-STMH and CMFH are unsupervised approaches that depend
-on data distributions, whereas the other six are supervised
-methods that take semantic labels into account. For fair com-
-parison with shallow-structure-based baselines, we use the
-trainset of MIMIC-CXR to optimize a CNN-F network for
-classification and extract 4096-dimensional features to rep-
-resent X-ray images. We set η= 1,β= 0.1andγ= 1
-for MIMIC-CXR as hyper-parameters. In the optimization
-phase, the batch size is set as 128 and three Adam solvers
-with different learning rates are applied (i.e., 10−3for lab-
-Net,10−4.5for imgNet and 10−3.5for txtNet).
-Mean average precision (MAP) is adopted to evaluate the
-performance of hashing-based CMR methods. MAP is the
-most widely used criteria metric to measure retrieval accu-
-racy, which is computed as follows:
-MAP =1
-|Q||Q|/summationdisplay
-i=11
-rqiR/summationdisplay
-j=1Pqi(j)δqi(j), (18)
-where|Q|indicates the number of query set, rqirepresents
-the number of correlated instances of query qiin database
-set,Ris the retrieval radius, Pqi(j)denotes the precision ofAlgorithm 1: The Optimization Procedure of DSIBH
-Input : X-ray images X1, radiology reports X2, semantic
-labels Y, learning rates λy,λ1,λ2, and iteration numbers
-Ty,T1,T2.
-Output : Parameters θ1andθ2of imgNet and
-txtNet.
-1:Randomly initialize θy,θ1,θ2andGy.
-2:repeat
-3: foriter=1 toTydo
-4: Updateθyby BP algorithm:
-θy←θy−λy·∇θyLy
-5: Update Gyby Eq. (16)
-6: end for
-7: foriter=1 toT1do
-8: Updateθ1by BP algorithm:
-θ1←θ1−λ1·∇θ1L1
-9: end for
-10: foriter=1 toT2do
-11: Updateθ2by BP algorithm:
-θ2←θ2−λ2·∇θ2L2
-12: end for
-13:until Convergence
-the topjretrieved sample and δqi(j)indicates whether the
-jthreturned sample is correlated with the ithquery entity. To
-reflect the overall property of rankings, the size of database
-set is used as the retrieval radius.
-The efficacy of DSIBH in CMR-based CAD
-CMR-based CAD stresses on two retrieval directions: using
-X-ray images to retrieve radiology reports ( X→R) and
-using radiology reports to retrieve X-ray images ( R→X).
-In experiments, we set bit length as 16, 32, 64 and 128 bits.
-Table 1 reports the MAP results on the MIMIC-CXR
-dataset. As can be seen, unsupervised methods fail to pro-
-vide reasonable retrieval results due to the neglect of se-
-mantic information. CCA performs the worst among these
-unsupervised methods due to the naive management of data
-distribution. Compared with CCA, STMH and CMFH can
-achieve a better retrieval accuracy, which we argue can
-be attributed to the coverage of data correlation. By con-
-trast, shallow-structure-based supervised methods includ-
-ing CMSSH, SCM, and SePH achieve a large performance
-gain over unsupervised methods by further considering se-
-mantic information to express semantic similarity with hash
-codes. Benefiting from the effect of nonlinear fitting abil-
-ity and self-adjusting feature extraction ability, deep super-
-vised methods including DCMH, SSAH and EGDH out-
-perform the six shallow methods in the mass. Due to the
-extra consideration of superfluous information reduction,
-our DSIBH can achieve the best accuracy. Specifically,
-compared with the recently proposed deep hashing method
-EGDH by MAP, our DSIBH achieves average absolute in-
-creases of 0.96%/0.47% on the MIMIC-CXR dataset.
-Meanwhile, we also visualize the top 4 retrieved medical
-profiles of our DSIBH on X→RandR→Xdirections
-using the MIMIC-CXR dataset in Figure 1. These resultsconfirm our concern that DSIBH can retrieve pathology-
-related heterogeneous medical data again.
-Conclusion
-In this paper, to preform computer-aided diagnosis (CAD)
-based on the large-scale multi-modal medical data, the
-cross-modal retrieval (CMR) technique based on semantic
-hashing is introduced. Inspired by Deep Deterministic In-
-formation Bottleneck, a novel method named Deep Super-
-vised Information Bottleneck Hashing (DSIBH) is designed
-to perform CMR-based CAD. Experiments are conducted
-on the large-scale medical dataset MIMIC-CXR. Compared
-with other state-of-the arts, our DSIBH can reduce the dis-
-traction of superfluous information, which thus strengthens
-the discriminability of hash codes in CMR-based CAD.
-Acknowledgements
-This work is partially supported by NSFC (62101179,
-61772220), Key R&D Plan of Hubei
-Province (2020BAB027) and Project of Hubei Univer-
-sity School (202011903000002).
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txt/2205.10663.txt

@@ -1,265 +0,0 @@
-Transformer based Generative Adversarial
-Network for Liver Segmentation
-Ugur Demir*, Zheyuan Zhang*, Bin Wang, Matthew Antalek, Elif Keles,
-Debesh Jha, Amir Borhani, Daniela Ladner, and Ulas Bagci
-Northwestern University, IL 60201, USA
-∗[email protected]
-Abstract. Automated liver segmentation from radiology scans (CT,
-MRI) can improve surgery and therapy planning and follow-up assess-
-ment in addition to conventional use for diagnosis and prognosis. Al-
-though convolutional neural networks (CNNs) have became the stan-
-dard image segmentation tasks, more recently this has started to change
-towards Transformers based architectures because Transformers are tak-
-ing advantage of capturing long range dependence modeling capability in
-signals, so called attention mechanism. In this study, we propose a new
-segmentation approach using a hybrid approach combining the Trans-
-former(s) with the Generative Adversarial Network (GAN) approach.
-The premise behind this choice is that the self-attention mechanism of the
-Transformers allows the network to aggregate the high dimensional fea-
-ture and provide global information modeling. This mechanism provides
-better segmentation performance compared with traditional methods.
-Furthermore, we encode this generator into the GAN based architecture
-so that the discriminator network in the GAN can classify the credibility
-of the generated segmentation masks compared with the real masks com-
-ing from human (expert) annotations. This allows us to extract the high
-dimensional topology information in the mask for biomedical image seg-
-mentation and provide more reliable segmentation results. Our model
-achieved a high dice coecient of 0.9433, recall of 0.9515, and preci-
-sion of 0.9376 and outperformed other Transformer based approaches.
-The implementation details of the proposed architecture can be found
-athttps://github.com/UgurDemir/tranformer_liver_segmentation .
-Keywords: Liver segmentation ·Transformer ·Generative adversarial
-network
-1 Introduction
-Liver cancer is among the leading causes of cancer-related deaths, accounting for
-8.3% of cancer mortality [14]. The high variability in shape, size, appearance,
-and local orientations makes liver (and liver diseases such as tumors, brosis)
-challenging to analyze from radiology scans for which the image segmentation is
-∗Those authors contribute equally to this paper.arXiv:2205.10663v2  [eess.IV]  28 May 20222 Demir, Zhang et al.
-often necessary [3]. An accurate organ and lesion segmentation could facilitate
-reliable diagnosis and therapy planning including prognosis [5].
-As a solution to biomedical image segmentation, the literature is vast and
-rich. The self-attention mechanism is nowadays widely used in the biomedical
-image segmentation eld where long-range dependencies and context dependent
-features are essential. By capturing such information, transformer based seg-
-mentation architectures (for example, SwinUNet [2]) have achieved promising
-performance on many vision tasks including biomedical image segmentation [7,
-15].
-In parallel to the all advances in Transformers, generative methods have
-achieved remarkable progresses in almost all elds of computer vision too [4].
-For example, Generative Adversarial Networks (GAN) [6] is a widely used tool
-for generating one target image from one source image. GAN has been applied to
-the image segmentation framework to distinguish the credibility of the generated
-masks like previous studies [11, 9]. The high dimensional topology information
-is an important feature for pixel levell classication, thus segmentation. For ex-
-ample, the segmented mask should recognize the object location, orientation,
-and scale prior to delineation procedure, but most current segmentation en-
-gines are likely to provide false positives outside the target region or conversely
-false negatives within the target region due to an inappropriate recognition of
-the target regions. By introducing the discriminator architecture (as a part of
-GAN) to distinguish whether the segmentation mask is high quality or not, we
-could proactively screen poor predictions from the segmentation model. Fur-
-thermore, this strategy can also allow us to take advantage of many unpaired
-segmentation masks which can be easily acquired or even simulated in the seg-
-mentation targets. To this end, in this paper, we propose a Transformer based
-GAN architecture as well as a Transformer based CycleGAN architecture for au-
-tomatic liver segmentation, a very improtant clinical precursor for liver diseases.
-By combining two strong algorithms, we aim to achieve both good recognition
-(localization) of the target region and high quality delineations.
-2 Proposed method
-We rst investigated the transformer architecture to solve the liver segmenta-
-tion problem from radiology scans, CT in particular due to its widespread use
-and being the rst choice in most liver disease quantication. The self-attention
-mechanism of the Transformers has been demonstrated to be very eective ap-
-proach when nding long range dependencies as stated before. This can be quite
-benecial for the liver segmentation problem especially because the object of
-interest (liver) is large and pixels constituting the same object are far from each
-other. We also utilized an adversarial training approach to boost the segmenta-
-tion model performance. For this, we have devised a conditional image generator
-in a vanilla-GAN that learns a mapping between the CT slices and the segmen-
-tation maps (i.e., surrogate of the truths or reference standard). The adversarial
-training forces the generator model to predict more realistic segmentation out-Transformer based Generative Adversarial Network for Liver Segmentation 3
-Ground 
-Truth 
-Predicted 
-Mask 
-Real 
-Fake Discriminator Transformer 
-Blocks Transformer 
-(Generator) 
-Decoder Encoder 
-Transformer 
-Blocks Image to Mask 
-Generator 
-Decoder Encoder Transformer 
-Blocks Mask to Image 
-Generator 
-Decoder Encoder 
-Real 
-Mask 
-Fake 
-Mask Mask 
-Discriminator Real 
-Image 
-Fake 
-Image Image 
-Discriminator 
-Ground 
-Truth 
-Input Input 
-Predicted 
-Mask 
-Input a) Transformer-GAN 
-b) Transformer-CycleGAN 
-Fig. 1: Block diagram of the Transformer GAN. (a) Vanilla GAN and (b) Cycle-
-GAN with Transformer generator architectures.
-comes. In addition to vanilla-GAN, we have also utilized the CycleGAN [17], [13]
-approach to investigate the eect of cycle consistency constraint on the segmen-
-tation task. Figure 1 demonstrates the general overview of the proposed method.
-2.1 Transformer based GAN
-Like other GAN architectures [10], Transformer based GAN architecture is com-
-posed of two related sub-architectures: the generator and the discriminator. The
-generator part could generate the segmentation mask from the raw image (i.e.,
-segmentation task itself), while the discriminator tries to distinguish predictions
-from the human annotated ground truth. GAN provides a better way to dis-
-tinguish the high-dimensional morphology information. The discriminator can
-provide the similarity between the predicted masks and the ground truth (i.e.,
-surrogate truth) masks. Vanilla GAN considers the whole segmentation to decide
-whether it is fake or not.
-2.2 Transformer based CycleGAN
-One alternative extension to the standard GAN approach is to use transformer
-based segmentation model within the CycleGAN setup. Unlike a standard GAN,
-CycleGAN consists of two generators and two discriminator networks. While the4 Demir, Zhang et al.
-rst generator accepts the raw images as input and predicts the segmentation
-masks, the second generator takes the predicted segmentation maps as input
-and maps them back to the input image. The rst discriminator classies the
-segmentation masks as either real or fake, and the second discriminator distin-
-guishes the real and the reconstructed image. Figure 1 illustrates this procedure
-with liver segmentation from CT scans.
-To embed transformers within the CycleGAN, we utilized the encoder-decoder
-style convolutional transformer model [13]. The premise behind this idea was
-that the encoder module takes the input image and decreases the spatial dimen-
-sions while extracting features with convolution layers. This allowed processing
-of large-scale images. The core transformer module consisted of several stacked
-linear layers and self-attention blocks. The decoder part increased the spatial
-dimension of the intermediate features and makes the nal prediction. For the
-discriminator network, we tried three convolutional architectures. The vanilla-
-GAN discriminator evaluates the input image as a whole. Alternatively, we have
-adopted PatchGAN discriminator architecture [8] to focus on small mask patches
-to decide the realness of each region. It splits the input masks into NxN regions
-and asses their quality individually. When we set the patch size to a pixel,
-PatchGAN can be considered as pixel level discriminator. W have observed that
-the pixel level discriminator tends to surpass other architecture for segmenta-
-tion. Figure 1 demonstrates the network overview. In all of the experiments,
-the segmentation model uses the same convolutional transformer and pixel level
-discriminator architectures.
-3 Experimental setup
-We have used Liver Tumor Segmentation Challenge (LiTS)[1] dataset. LiTS
-consists of 131 CT scans. This dataset is publicly available under segmentation
-challenge website and approved IRB by the challenge organizers. More informa-
-tion about the dataset and challenge can be found here1.
-All our models were trained on NVIDIA RTX A6000 GPU after implemented
-using the PyTorch [12] framework. We have used 95 samples for training and
-36 samples for testing. All models are trained on the same hyperparameters
-conguration with a learning rate of 2 e4, and Adam optimizer with beta1
-being 0.5 and beta2 being 0.999. All of the discriminators use the pixel level
-discriminator in both GAN and CycleGAN experiments. We have used recall,
-precision, and dice coecient for quantitative evaluations of the segmentation.
-Further, segmentation results were qualitatively evaluated by the participating
-physicians. Our algorithms are available for public use.
-4 Results
-We presented the evaluation results in Table 1. Our best performing method
-was Transformer based GAN architecture, achieved a highest dice coecient
-1https://competitions.codalab.org/competitions/17094#learn_the_detailsTransformer based Generative Adversarial Network for Liver Segmentation 5
-Input Segmentation 
- Input Segmentation 
-Fig. 2: Transformer based GAN liver segmentation results. Green: True positive,
-Red: False Positive, Blue: False Negative.
-Table 1: Performance of Transformer based methods on the LITS dataset. [1]
-Method Dice coecient Precision Recall
-Transformer [16, 13] 0.9432 0.9464 0.9425
-Transformer - CycleGAN (ours) 0.9359 0.9539 0.9205
-Transformer - GAN (ours) 0.9433 0.9376 0.9515
-of 0.9433 and recall rate of 0.9515. Similarly, our transformer based CycleGAN
-architecture has the highest precision, 0.9539. With Transformer based GAN, we
-achieved 0.9% improvement in recall and 0.01% improvement in dice coecient
-with respect to the vanilla Transformers. It is to be noted that we have used also
-post-processing technique which boosts the performance for "all" the baselines
-to avoid biases one from each other.6 Demir, Zhang et al.
-Figure 2 shows our qualitative results for the liver segmentation. We have ex-
-amined all the liver segmentation results one-by-one and no failure were identied
-by the participating physicians. Hence, visual results agreed with the quantita-
-tive results as described in Table 1.
-5 Conclusion
-In this study, we explored the use of transformer-based GAN architectures for
-medical image segmentation. Specically, we used a self-attention mechanism
-and designed a discriminator for classifying the credibility of generated segmen-
-tation masks. Our experimental result showed that the proposed new segmenta-
-tion architectures could provide accurate and reliable segmentation performance
-as compared to the baseline Transfomers. Although we have shown our results
-in an important clinical problem for liver diseases where image-based quanti-
-cation is vital, the proposed hybrid architecture (i.e., combination of GAN and
-Transformers) can potentially be applied to various medical image segmentation
-tasks beyond liver CTs as the algorithms are generic, reproducible, and carries
-similarities with the other segmentation tasks in biomedical imaging eld. We
-anticipate that our architecture can also be applied to medical scans within the
-semi-supervised learning, planned as a future work.
-Acknowledgement
-This study is partially supported by NIH NCI grants R01-CA246704 and R01-
-R01-CA240639.
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