2 papers
cs.LG2026
EchoAlign: Bridging Generative and Discriminative Learning under Noisy Labels
Yuxiang Zheng, Zhongyi Han, Yilong Yin
Noisy labels severely hinder the accuracy and generalization of machine learning models, especially when ambiguous instance features make reliable annotation difficult. Existing ap…
cs.LG2024
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
Fan Wang, Zhongyi Han, Xingbo Liu +2
In domain adaptation, there are two popular paradigms: Unsupervised Domain Adaptation (UDA), which aligns distributions using source data, and Source-Free Domain Adaptation (SFDA),…