Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets

• URL: http://arxiv.org/abs/2201.12765v1
• Date: Sun, 30 Jan 2022 09:36:19 GMT
• Title: Improving Corruption and Adversarial Robustness by Enhancing Weak Subnets
• Authors: Yong Guo, David Stutz, Bernt Schiele
• Abstract summary: We propose a novel robust training method which explicitly identifies and enhances weaks during training to improve robustness. Specifically, we develop a search algorithm to find particularly weaks and propose to explicitly strengthen them via knowledge distillation from the full network. We show that our EWS greatly improves the robustness against corrupted images as well as the accuracy on clean data.
• Score: 91.9346332103637
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have achieved great success in many computer vision tasks. However, deep networks have been shown to be very susceptible to corrupted or adversarial images, which often result in significant performance drops. In this paper, we observe that weak subnetwork (subnet) performance is correlated with a lack of robustness against corruptions and adversarial attacks. Based on that observation, we propose a novel robust training method which explicitly identifies and enhances weak subnets (EWS) during training to improve robustness. Specifically, we develop a search algorithm to find particularly weak subnets and propose to explicitly strengthen them via knowledge distillation from the full network. We show that our EWS greatly improves the robustness against corrupted images as well as the accuracy on clean data. Being complementary to many state-of-the-art data augmentation approaches, EWS consistently improves corruption robustness on top of many of these approaches. Moreover, EWS is also able to boost the adversarial robustness when combined with popular adversarial training methods.

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