13 citations · 36 across the 7 of their papers we have counts for
4 papers · 1 filter
Strength-Adaptive Adversarial Training
Chaojian Yu, Dawei Zhou, Li Shen +5
Adversarial training (AT) is proved to reliably improve network's robustness against adversarial data. However, current AT with a pre-specified perturbation budget has limitations…
Harnessing Out-Of-Distribution Examples via Augmenting Content and Style
Zhuo Huang, Xiaobo Xia, Li Shen +4
Machine learning models are vulnerable to Out-Of-Distribution (OOD) examples, and such a problem has drawn much attention. However, current methods lack a full understanding of dif…
Understanding Robust Overfitting of Adversarial Training and Beyond
Chaojian Yu, Bo Han, Li Shen +4
Robust overfitting widely exists in adversarial training of deep networks. The exact underlying reasons for this are still not completely understood. Here, we explore the causes of…
Robust Weight Perturbation for Adversarial Training
Chaojian Yu, Bo Han, Mingming Gong +4
Overfitting widely exists in adversarial robust training of deep networks. An effective remedy is adversarial weight perturbation, which injects the worst-case weight perturbation…