3 papers
cs.LG2026
Breaking the Prototype Bias Loop: Confidence-Aware Federated Contrastive Learning for Highly Imbalanced Clients
Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen +4
Local class imbalance and data heterogeneity across clients often trap prototype-based federated contrastive learning in a prototype bias loop: biased local prototypes induced by i…
cs.LG2025
Compressing Model with Few Class-Imbalance Samples: An Out-of-Distribution Expedition
Tian-Shuang Wu, Shen-Huan Lyu, Ning Chen +2
In recent years, as a compromise between privacy and performance, few-sample model compression has been widely adopted to deal with limited data resulting from privacy and security…
cs.LG2025
Improving Multi-Label Contrastive Learning by Leveraging Label Distribution
Ning Chen, Shen-Huan Lyu, Tian-Shuang Wu +2
In multi-label learning, leveraging contrastive learning to learn better representations faces a key challenge: selecting positive and negative samples and effectively utilizing la…