text: Strengths: * Very simple approach, amounting to coupled training of "e" identical copies  of a chosen net architecture, whose predictions are fused during training.  This forces the different model instances to become more complementary. * Perhaps counterintuitively, experiments also show that coupled ensembling leads to individual nets that perform better than those produced by separate training. * The practical advantages of the proposed approach are twofold: 1. Given a fixed parameter budget, coupled ensembling leads to better accuracy than a single net or an ensemble of disjointly-trained nets. 2. For the same accuracy, coupled ensembling yields significant parameter savings. Weaknesses: * Although results are very strong,  the proposed models do not outperform the state-of-the-art, except for the models reported in Table 4,  which however were obtained by *traditional* ensembling of coupled ensembles.  * Coupled ensembling requires joint training of all nets in the ensemble  and thus is limited by the size of the model that can be fit in memory.  Conversely, traditional ensembling involves separate training of the different instances  and this enables the learning of an arbitrary number of individual nets.  * I am surprised by the results in Table 2,  which suggest that the optimal number of nets in the ensemble is remarkably low (only 3!).  It'd be valuable to understand whether this kind of result holds for other network architectures or whether it is specific to this choice of net. * Strictly speaking it is correct to refer to the individual nets in the ensembles as "branches" and "basic blocks."  Nevertheless, I find the use of these terms confusing in the context of the proposed approach,  since they are commonly used to denote concepts different from those represented here.   I would recommend refraining from using these terms here. Overall, the paper provides limited technical novelty.  Yet, it reveals some interesting empirical findings about the benefits of coordinated training of models in an ensemble.