149 citations · 152 across the 4 of their papers we have counts for
4 papers
Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network
Huan Hua, Jun Yan, Xi Fang +3
The information bottleneck (IB) method is a feasible defense solution against adversarial attacks in deep learning. However, this method suffers from the spurious correlation, whic…
Wavelet Regularization Benefits Adversarial Training
Jun Yan, Huilin Yin, Xiaoyang Deng +4
Adversarial training methods are state-of-the-art (SOTA) empirical defense methods against adversarial examples. Many regularization methods have been proven to be effective with t…
Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
Wei Zhou, Dong Chen, Jun Yan +3
Autonomous driving has attracted significant research interests in the past two decades as it offers many potential benefits, including releasing drivers from exhausting driving an…
On Procedural Adversarial Noise Attack And Defense
Jun Yan, Xiaoyang Deng, Huilin Yin +1
Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images. Rese…