Stable ResNet

Deep ResNet architectures have achieved state of the art performance on many tasks. While they solve the problem of gradient vanishing, they might suffer from gradient exploding as the depth becomes large (Yang et al. 2017). Moreover, recent results have shown that ResNet might lose expressivity as...

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Bibliographic Details
Main Authors Hayou, Soufiane, Clerico, Eugenio, He, Bobby, Deligiannidis, George, Doucet, Arnaud, Rousseau, Judith
Format Journal Article
LanguageEnglish
Published 24.10.2020
Subjects
Computer Science - Learning
Statistics - Machine Learning
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Summary:Deep ResNet architectures have achieved state of the art performance on many tasks. While they solve the problem of gradient vanishing, they might suffer from gradient exploding as the depth becomes large (Yang et al. 2017). Moreover, recent results have shown that ResNet might lose expressivity as the depth goes to infinity (Yang et al. 2017, Hayou et al. 2019). To resolve these issues, we introduce a new class of ResNet architectures, called Stable ResNet, that have the property of stabilizing the gradient while ensuring expressivity in the infinite depth limit.
DOI:10.48550/arxiv.2010.12859