4 papers · 1 filter
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…
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…
Enhance Learning Efficiency of Oblique Decision Tree via Feature Concatenation
Shen-Huan Lyu, Yi-Xiao He, Yanyan Wang +3
Oblique Decision Tree (ODT) separates the feature space by linear projections, as opposed to the conventional Decision Tree (DT) that forces axis-parallel splits. ODT has been prov…
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…