5 citations · 12 across the 6 of their papers we have counts for
6 papers
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…
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…
MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial Beauty Prediction
Luojun Lin, Zhifeng Shen, Jia-Li Yin +3
Predicting individual aesthetic preferences holds significant practical applications and academic implications for human society. However, existing studies mainly focus on learning…
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…