154 citations · 418 across the 22 of their papers we have counts for
36 papers · 1 filter
Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks
Jianan Zhou, Jianing Zhu, Jingfeng Zhang +4
Adversarial training (AT) with imperfect supervision is significant but receives limited attention. To push AT towards more practical scenarios, we explore a brand new yet challeng…
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…
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…
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Quanming Yao, Yaqing Wang, Bo Han +1
Nonconvex regularization has been popularly used in low-rank matrix learning. However, extending it for low-rank tensor learning is still computationally expensive. To address this…
Do We Need to Penalize Variance of Losses for Learning with Label Noise?
Yexiong Lin, Yu Yao, Yuxuan Du +4
Algorithms which minimize the averaged loss have been widely designed for dealing with noisy labels. Intuitively, when there is a finite training sample, penalizing the variance of…
Local Reweighting for Adversarial Training
Ruize Gao, Feng Liu, Kaiwen Zhou +3
Instances-reweighted adversarial training (IRAT) can significantly boost the robustness of trained models, where data being less/more vulnerable to the given attack are assigned sm…