Skip to yearly menu bar Skip to main content


A Kernel Perspective for Regularizing Deep Neural Networks

Alberto Bietti · Gregoire Mialon · Dexiong Chen · Julien Mairal

Pacific Ballroom #223

Keywords: [ Kernel Methods ] [ Algorithms ]


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.

Live content is unavailable. Log in and register to view live content