222 citations · 555 across the 35 of their papers we have counts for
5 papers · 1 filter
Learning Defense Transformers for Counterattacking Adversarial Examples
Jincheng Li, Jiezhang Cao, Yifan Zhang +2
Deep neural networks (DNNs) are vulnerable to adversarial examples with small perturbations. Adversarial defense thus has been an important means which improves the robustness of D…
Internal Wasserstein Distance for Adversarial Attack and Defense
Qicheng Wang, Shuhai Zhang, Jiezhang Cao +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial attacks that would trigger misclassification of DNNs but may be imperceptible to human perception. Adversarial…
Improving Generative Adversarial Networks with Local Coordinate Coding
Jiezhang Cao, Yong Guo, Qingyao Wu +3
Generative adversarial networks (GANs) have shown remarkable success in generating realistic data from some predefined prior distribution (e.g., Gaussian noises). However, such pri…
Online Adaptive Asymmetric Active Learning with Limited Budgets
Yifan Zhang, Peilin Zhao, Shuaicheng Niu +4
Online Active Learning (OAL) aims to manage unlabeled datastream by selectively querying the label of data. OAL is applicable to many real-world problems, such as anomaly detection…
Multi-marginal Wasserstein GAN
Jiezhang Cao, Langyuan Mo, Yifan Zhang +3
Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as…