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REVE: Regularizing Deep Learning with Variational Entropy Bound

Abstract : Studies on generalization performance of machine learning algorithms under the scope of information theory suggest that compressed representations can guarantee good generalization, inspiring many compression-based regularization methods. In this paper, we introduce REVE, a new regularization scheme. Noting that compressing the representation can be sub-optimal, our first contribution is to identify a variable that is directly responsible for the final prediction. Our method aims at compressing the class conditioned entropy of this latter variable. Second, we introduce a variational upper bound on this conditional entropy term. Finally, we propose a scheme to instantiate a tractable loss that is integrated within the training procedure of the neural network and demonstrate its efficiency on different neural networks and datasets.
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Submitted on : Tuesday, October 15, 2019 - 4:49:33 PM
Last modification on : Sunday, June 26, 2022 - 2:41:34 AM

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Antoine Saporta, Yifu Chen, Michael Blot, Matthieu Cord. REVE: Regularizing Deep Learning with Variational Entropy Bound. 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.1610-1614, ⟨10.1109/ICIP.2019.8804396⟩. ⟨hal-02316946⟩



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