10 citations · 26 across the 11 of their papers we have counts for
11 papers
Towards the Desirable Decision Boundary by Moderate-Margin Adversarial Training
Xiaoyu Liang, Yaguan Qian, Jianchang Huang +4
Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep lea…
Hessian-Free Second-Order Adversarial Examples for Adversarial Learning
Yaguan Qian, Yuqi Wang, Bin Wang +3
Recent studies show deep neural networks (DNNs) are extremely vulnerable to the elaborately designed adversarial examples. Adversarial learning with those adversarial examples has…
ViR:the Vision Reservoir
Xian Wei, Bin Wang, Mingsong Chen +7
The most recent year has witnessed the success of applying the Vision Transformer (ViT) for image classification. However, there are still evidences indicating that ViT often suffe…
Learning Modified Indicator Functions for Surface Reconstruction
Dong Xiao, Siyou Lin, Zuoqiang Shi +1
Surface reconstruction is a fundamental problem in 3D graphics. In this paper, we propose a learning-based approach for implicit surface reconstruction from raw point clouds withou…
Progressive residual learning for single image dehazing
Yudong Liang, Bin Wang, Jiaying Liu +3
The recent physical model-free dehazing methods have achieved state-of-the-art performances. However, without the guidance of physical models, the performances degrade rapidly when…
Towards Speeding up Adversarial Training in Latent Spaces
Yaguan Qian, Qiqi Shao, Tengteng Yao +5
Adversarial training is wildly considered as one of the most effective way to defend against adversarial examples. However, existing adversarial training methods consume unbearable…