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PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization by Xiao, Jiancong; Sun, Ruoyu; Luo, Zhi- Quan is a scholarly article available to read on EtoBox.

What is PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization about?

Deep neural networks (DNNs) are vulnerable to adversarial attacks. It is found empirically that adversarially robust generalization is crucial in establishing defense algorithms against adversarial attacks. Therefore, it is interesting to study the theoretical guarantee of robust generalization. This paper focuses on norm-based complexity, based on a PAC-Bayes approach (Neyshabur et al., 2017). The main challenge lies in extending the key ingredient, which is a weight perturbation bound in standard settings, to the robust settings. Existing attempts heavily rely on additional strong assumptions, leading to loose bounds. In this paper, we address this issue and provide a spectrally-normalized robust generalization bound for DNNs. Compared to existing bounds, our bound offers two significant advantages: Firstly, it does not depend on additional assumptions. Secondly, it is considerably tighter, aligning with the bounds of standard generalization. Therefore, our result provides a different perspective on understanding robust generalization: The mismatch terms between standard and robust generalization bounds shown in previous studies do not contribute to the poor robust generalization

Author

Xiao, Jiancong; Sun, Ruoyu; Luo, Zhi- Quan

Published

2023

Language

EN