A Kernel Perspective for Regularizing Deep Neural Networks

Alberto Bietti 1 Grégoire Mialon 1, 2 Dexiong Chen 1 Julien Mairal 1
1 Thoth - Apprentissage de modèles à partir de données massives
LJK - Laboratoire Jean Kuntzmann, Inria Grenoble - Rhône-Alpes
2 SIERRA - Statistical Machine Learning and Parsimony
DI-ENS - Département d'informatique de l'École normale supérieure, CNRS - Centre National de la Recherche Scientifique, Inria de Paris
Abstract : We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS). Even though this norm cannot be computed, it admits upper and lower approximations leading to various practical strategies. Specifically, this perspective (i) provides a common umbrella for many existing regularization principles, including spectral norm and gradient penalties, or adversarial training, (ii) leads to new effective regularization penalties, and (iii) suggests hybrid strategies combining lower and upper bounds to get better approximations of the RKHS norm. We experimentally show this approach to be effective when learning on small datasets, or to obtain adversarially robust models.
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Contributor : Alberto Bietti <>
Submitted on : Thursday, January 24, 2019 - 7:05:35 PM
Last modification on : Wednesday, May 15, 2019 - 6:13:53 PM


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  • HAL Id : hal-01884632, version 3
  • ARXIV : 1810.00363


Alberto Bietti, Grégoire Mialon, Dexiong Chen, Julien Mairal. A Kernel Perspective for Regularizing Deep Neural Networks. 2019. ⟨hal-01884632v3⟩



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