21 citations · 54 across the 6 of their papers we have counts for
6 papers
When Source-Free Domain Adaptation Meets Learning with Noisy Labels
Li Yi, Gezheng Xu, Pengcheng Xu +5
Recent state-of-the-art source-free domain adaptation (SFDA) methods have focused on learning meaningful cluster structures in the feature space, which have succeeded in adapting t…
On Learning Fairness and Accuracy on Multiple Subgroups
Changjian Shui, Gezheng Xu, Qi Chen +5
We propose an analysis in fair learning that preserves the utility of the data while reducing prediction disparities under the criteria of group sufficiency. We focus on the scenar…
Learning High-quality Proposals for Acne Detection
Jianwei Zhang, Lei Zhang, Junyou Wang +4
Acne detection is crucial for interpretative diagnosis and precise treatment of skin disease. The arbitrary boundary and small size of acne lesions lead to a significant number of…
Evolving Domain Generalization
William Wei Wang, Gezheng Xu, Ruizhi Pu +6
Domain generalization aims to learn a predictive model from multiple different but related source tasks that can generalize well to a target task without the need of accessing any…
Fair Representation Learning through Implicit Path Alignment
Changjian Shui, Qi Chen, Jiaqi Li +2
We consider a fair representation learning perspective, where optimal predictors, on top of the data representation, are ensured to be invariant with respect to different sub-group…
Aggregating From Multiple Target-Shifted Sources
Changjian Shui, Zijian Li, Jiaqi Li +3
Multi-source domain adaptation aims at leveraging the knowledge from multiple tasks for predicting a related target domain. Hence, a crucial aspect is to properly combine different…