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Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization. (arXiv:2205.11744v1 [cs.LG])
May 25, 2022, 1:12 a.m. | Shudong Zhang, Haichang Gao, Tianwei Zhang, Yunyi Zhou, Zihui Wu
cs.CV updates on arXiv.org arxiv.org
Adversarial training (AT) has proven to be one of the most effective ways to
defend Deep Neural Networks (DNNs) against adversarial attacks. However, the
phenomenon of robust overfitting, i.e., the robustness will drop sharply at a
certain stage, always exists during AT. It is of great importance to decrease
this robust generalization gap in order to obtain a robust model. In this
paper, we present an in-depth study towards the robust overfitting from a new
angle. We observe that consistency …
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