Class Prototype-guided Disambiguation in Partially Labeled Learning

Partial Label Learning PLL is a prominent research direction in weak supervision, in which each instance is associated with a set of ambiguous candidate labels. Recent PLL methods primarily focus on uncovering the latent true label using label ambiguity information. However, the candidate label set contains only one true label. We directly utilize the entire candidate set will introduce label noise and hinder performance improvement of model training. To address this issue, we propose a guided model learning method called Class Prototype-induced Weighted Contrastive Partial Label Learning method PIWCL to effectively reduce the impact of label noise. Specifically, PIWCL consists of the Class Prototype-guided Module CPGM and the Weighted Contrastive Learning Module WCLM . WCLM employs a novel weighting scheme to learn more compact and discriminative representations, mitigating the confusion caused by ambiguous class samples while capturing useful latent information. Meanwhile, CPGM guides the classifier's learning process, further improving its ability to distinguish between positive and negative samples and facilitating the training of WCLM. Experimental results show that, compared to existing PLL methods, PIWCL achieves significant improvements in effectiveness.