2 citations · 9 across the 7 of their papers we have counts for
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cs.LG2024★ 2 cited
MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
Zhaozhe Hu, Jia-Li Yin, Bin Chen +3
Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous resear…
cs.LG2023★ 1 cited
Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data
Zekai Chen, Fuyi Wang, Zhiwei Zheng +2
Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, adversaries can manipul…
cs.LG2021★ 1 cited
Robust Single-step Adversarial Training with Regularizer
Lehui Xie, Yaopeng Wang, Jia-Li Yin +1
High cost of training time caused by multi-step adversarial example generation is a major challenge in adversarial training. Previous methods try to reduce the computational burden…