4 citations · 6 across the 2 of their papers we have counts for
2 papers
cs.CV2022★ 2 cited
Vanilla Feature Distillation for Improving the Accuracy-Robustness Trade-Off in Adversarial Training
Guodong Cao, Zhibo Wang, Xiaowei Dong +4
Adversarial training has been widely explored for mitigating attacks against deep models. However, most existing works are still trapped in the dilemma between higher accuracy and…
cs.LG2022★ 4 cited
Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Models
Zhibo Wang, Xiaowei Dong, Henry Xue +4
Prioritizing fairness is of central importance in artificial intelligence (AI) systems, especially for those societal applications, e.g., hiring systems should recommend applicants…