27 citations · 247 across the 44 of their papers we have counts for
22 papers · 1 filter
Clustering Effect of (Linearized) Adversarial Robust Models
Yang Bai, Xin Yan, Yong Jiang +2
Adversarial robustness has received increasing attention along with the study of adversarial examples. So far, existing works show that robust models not only obtain robustness aga…
Fooling Adversarial Training with Inducing Noise
Zhirui Wang, Yifei Wang, Yisen Wang
Adversarial training is widely believed to be a reliable approach to improve model robustness against adversarial attack. However, in this paper, we show that when trained on one t…
A Unified Game-Theoretic Interpretation of Adversarial Robustness
Jie Ren, Die Zhang, Yisen Wang +8
This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i.e.} the view of multi-order interactions between input variables of DNNs. B…
Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks
Chen Ma, Xiangyu Guo, Li Chen +2
One major problem in black-box adversarial attacks is the high query complexity in the hard-label attack setting, where only the top-1 predicted label is available. In this paper,…
On Training Implicit Models
Zhengyang Geng, Xin-Yu Zhang, Shaojie Bai +2
This paper focuses on training implicit models of infinite layers. Specifically, previous works employ implicit differentiation and solve the exact gradient for the backward propag…
Residual Relaxation for Multi-view Representation Learning
Yifei Wang, Zhengyang Geng, Feng Jiang +4
Multi-view methods learn representations by aligning multiple views of the same image and their performance largely depends on the choice of data augmentation. In this paper, we no…