2 citations · 9 across the 7 of their papers we have counts for
4 papers · 1 filter
Towards Trustworthy Unsupervised Domain Adaptation: A Representation Learning Perspective for Enhancing Robustness, Discrimination, and Generalization
Jia-Li Yin, Haoyuan Zheng, Ximeng Liu
Robust Unsupervised Domain Adaptation (RoUDA) aims to achieve not only clean but also robust cross-domain knowledge transfer from a labeled source domain to an unlabeled target dom…
An Adaptive Model Ensemble Adversarial Attack for Boosting Adversarial Transferability
Bin Chen, Jia-Li Yin, Shukai Chen +2
While the transferability property of adversarial examples allows the adversary to perform black-box attacks (i.e., the attacker has no knowledge about the target model), the trans…
SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation
Wanqing Zhu, Jia-Li Yin, Bo-Hao Chen +1
As acquiring manual labels on data could be costly, unsupervised domain adaptation (UDA), which transfers knowledge learned from a rich-label dataset to the unlabeled target datase…
Push Stricter to Decide Better: A Class-Conditional Feature Adaptive Framework for Improving Adversarial Robustness
Jia-Li Yin, Lehui Xie, Wanqing Zhu +2
In response to the threat of adversarial examples, adversarial training provides an attractive option for enhancing the model robustness by training models on online-augmented adve…