Timezone: »
Spotlight
Adversarial Training is a Form of Data-dependent Operator Norm Regularization
Kevin Roth · Yannic Kilcher · Thomas Hofmann
Wed Dec 09 07:20 AM -- 07:30 AM (PST) @ Orals & Spotlights: Social/Adversarial Learning
We establish a theoretical link between adversarial training and operator norm regularization for deep neural networks. Specifically, we prove that $l_p$-norm constrained projected gradient ascent based adversarial training with an $l_q$-norm loss on the logits of clean and perturbed inputs is equivalent to data-dependent (p, q) operator norm regularization. This fundamental connection confirms the long-standing argument that a network’s sensitivity to adversarial examples is tied to its spectral properties and hints at novel ways to robustify and defend against adversarial attacks. We provide extensive empirical evidence on state-of-the-art network architectures to support our theoretical results.
Author Information
Kevin Roth (ETH Zurich)
Yannic Kilcher (ETH Zurich)
Thomas Hofmann (ETH Zurich)
Related Events (a corresponding poster, oral, or spotlight)
-
2020 Poster: Adversarial Training is a Form of Data-dependent Operator Norm Regularization »
Wed Dec 9th 05:00 -- 07:00 PM Room Poster Session 3
More from the Same Authors
-
2020 Poster: Batch normalization provably avoids ranks collapse for randomly initialised deep networks »
Hadi Daneshmand · Jonas Kohler · Francis Bach · Thomas Hofmann · Aurelien Lucchi -
2020 Poster: Convolutional Generation of Textured 3D Meshes »
Dario Pavllo · Graham Spinks · Thomas Hofmann · Marie-Francine Moens · Aurelien Lucchi -
2020 Oral: Convolutional Generation of Textured 3D Meshes »
Dario Pavllo · Graham Spinks · Thomas Hofmann · Marie-Francine Moens · Aurelien Lucchi -
2019 Poster: A Domain Agnostic Measure for Monitoring and Evaluating GANs »
Paulina Grnarova · Kfir Y. Levy · Aurelien Lucchi · Nathanael Perraudin · Ian Goodfellow · Thomas Hofmann · Andreas Krause -
2018 Poster: Hyperbolic Neural Networks »
Octavian Ganea · Gary Becigneul · Thomas Hofmann -
2018 Spotlight: Hyperbolic Neural Networks »
Octavian Ganea · Gary Becigneul · Thomas Hofmann -
2018 Poster: Deep State Space Models for Unconditional Word Generation »
Florian Schmidt · Thomas Hofmann -
2017 Poster: Stabilizing Training of Generative Adversarial Networks through Regularization »
Kevin Roth · Aurelien Lucchi · Sebastian Nowozin · Thomas Hofmann -
2016 Poster: Scalable Adaptive Stochastic Optimization Using Random Projections »
Gabriel Krummenacher · Brian McWilliams · Yannic Kilcher · Joachim M Buhmann · Nicolai Meinshausen -
2016 Poster: Adaptive Newton Method for Empirical Risk Minimization to Statistical Accuracy »
Aryan Mokhtari · Hadi Daneshmand · Aurelien Lucchi · Thomas Hofmann · Alejandro Ribeiro -
2015 Poster: Variance Reduced Stochastic Gradient Descent with Neighbors »
Thomas Hofmann · Aurelien Lucchi · Simon Lacoste-Julien · Brian McWilliams -
2014 Poster: Communication-Efficient Distributed Dual Coordinate Ascent »
Martin Jaggi · Virginia Smith · Martin Takac · Jonathan Terhorst · Sanjay Krishnan · Thomas Hofmann · Michael Jordan