Learning with Partial Labels from Conflict-Free and Semi-Supervised Perspective

Abstract

Partial label learning is a prevalent weakly supervised learning paradigm. Despite the impressive performance achieved by existing methods (e.g., those based on self-training or semi-supervised learning (SSL)), they often suffer from error accumulation or inefficient data utilization. To address these, we aim to actively avoid errors at each training stage and fully leverage information from all available data, encompassing candidate and non-candidate labels. C-FreeMix consists of two stages and performs SSL from a conflict-free perspective. In the warm-up stage, we propose conflict-free negative learning to ensure nontoxic supervision signals along with rapid convergence capability. In the SSL stage, we define a Margin metric to select examples with less ambiguity as labeled ones precisely. Then, MixMatch is adopted with two improvements: label refinement and partial mixup, to utilize all available information. Extensive experiments demonstrate that C-FreeMix outperforms the current state-of-the-art methods.