Evaluation Report

Testing Data

Dataset: CIFAR-10 Test Set
Metrics: Forget class accuracy(loss), Retain class accuracy(loss)

Training Details

Training Procedure

Base Model: ResNet18
Dataset: CIFAR-10
Excluded Class: Varies by model
Loss Function: Negative Log-Likelihood Loss
Optimizer: SGD with:
- Learning rate: 0.1
- Momentum: 0.9
- Weight decay: 5e-4
- Nesterov: True
Scheduler: CosineAnnealingLR (T_max: 200)
Training Epochs: 62
Batch Size: 64
Hardware: Single GPU (NVIDIA GeForce RTX 3090)
Number of Retrain: 1

Algorithm

The CF-k algorithm was used for inexact unlearning. This method systematically removes the influence of a specific class from the model while retaining the ability to classify the remaining classes. Each resulting model (cifar10_resnet18_CF-k_X.pth) corresponds to a scenario where a single class (X) has been unlearned. The CF-k algorithm provides an efficient framework for evaluating the robustness and adaptability of models under inexact unlearning constraints.

For more details on the CF-k algorithm, refer to the GitHub repository.

Results

Model	Forget Class	Forget class acc(loss)	Retain class acc(loss)
cifar10_resnet18_CF-k_0.pth	Airplane	0.0 (4.659)	95.49 (0.168)
cifar10_resnet18_CF-k_1.pth	Automobile	0.0 (4.571)	95.34 (0.181)
cifar10_resnet18_CF-k_2.pth	Bird	0.0 (4.879)	95.89 (0.158)
cifar10_resnet18_CF-k_3.pth	Cat	0.0 (5.165)	96.56 (0.127)
cifar10_resnet18_CF-k_4.pth	Deer	0.0 (4.562)	95.52 (0.170)
cifar10_resnet18_CF-k_5.pth	Dog	0.0 (4.862)	96.30 (0.137)
cifar10_resnet18_CF-k_6.pth	Frog	0.0 (4.458)	95.37 (0.185)
cifar10_resnet18_CF-k_7.pth	Horse	0.0 (4.514)	95.23 (0.179)
cifar10_resnet18_CF-k_8.pth	Ship	0.0 (4.577)	95.38 (0.178)
cifar10_resnet18_CF-k_9.pth	Truck	0.0 (4.644)	95.53 (0.174)

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CF-k