3 papers
cs.LG2026
Enhance and Reuse: A Dual-Mechanism Approach to Boost Deep Forest for Label Distribution Learning
Jia-Le Xu, Shen-Huan Lyu, Yu-Nian Wang +4
Label distribution learning (LDL) requires the learner to predict the degree of correlation between each sample and each label. To achieve this, a crucial task during learning is t…
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…