3 papers
cs.LG2026
Prototype-Based Semantic Consistency Alignment for Domain Adaptive Retrieval
Tianle Hu, Weijun Lv, Na Han +4
Domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, enabling effective retrieval while mitigating domain discrepancies.…
cs.LG2026
Bringing Clustering to MLL: Weakly-Supervised Clustering for Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +2
Label noise in multi-label learning (MLL) poses significant challenges for model training, particularly in partial multi-label learning (PML) where candidate labels contain both re…
cs.LG2026
Feature-Label Modal Alignment for Robust Partial Multi-Label Learning
Yu Chen, Weijun Lv, Yue Huang +4
In partial multi-label learning (PML), each instance is associated with a set of candidate labels containing both ground-truth and noisy labels. The presence of noisy labels disrup…