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Stochastic Local Winner-Takes-All Networks Enable Profound Adversarial Robustness
Konstantinos Panousis · Sotirios Chatzis · Sergios Theodoridis
Event URL: https://openreview.net/forum?id=CcSPRnm1M5s »

This work explores the potency of stochastic competition-based activations, namely Stochastic Local Winner-Takes-All (LWTA), against powerful (gradient-based) white-box and black-box adversarial attacks; we especially focus on Adversarial Training settings. In our work, we replace the conventional ReLU-based nonlinearities with blocks comprising locally and stochastically competing linear units. The output of each network layer now yields a sparse output, depending on the outcome of winner sampling in each block. We rely on the Variational Bayesian framework for training and inference; we incorporate conventional PGD-based adversarial training arguments to increase the overall adversarial robustness. As we experimentally show, the arising networks yield state-of-the-art robustness against powerful adversarial attacks while retaining very high classification rate in the benign case.

Author Information

Konstantinos Panousis (Cyprus University of Technology)
Sotirios Chatzis (Cyprus University of Technology)
Sergios Theodoridis

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