23 citations · 26 across the 10 of their papers we have counts for
3 papers · 1 filter
AdvFunMatch: When Consistent Teaching Meets Adversarial Robustness
Zihui Wu, Haichang Gao, Bingqian Zhou +1
\emph{Consistent teaching} is an effective paradigm for implementing knowledge distillation (KD), where both student and teacher models receive identical inputs, and KD is treated…
Lower Difficulty and Better Robustness: A Bregman Divergence Perspective for Adversarial Training
Zihui Wu, Haichang Gao, Bingqian Zhou +2
In this paper, we investigate on improving the adversarial robustness obtained in adversarial training (AT) via reducing the difficulty of optimization. To better study this proble…
Alleviating Robust Overfitting of Adversarial Training With Consistency Regularization
Shudong Zhang, Haichang Gao, Tianwei Zhang +2
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 overf…