Title: Mutual Information Guided Diffusion Model for Partial Label Learning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Partial Label Learning;Diffusion Model;Mutual Information

Abstract: This paper proposes a novel paradigm for partial label learning (PLL) that integrates diffusion mechanisms with mutual information (MI) estimation to address the challenge of label disambiguation. In PLL, each training instance is typically associated with multiple candidate labels, among which only one is the ground truth. To simulate the process of label degradation, we introduce noise into the labels through forward diffusion to simulate candidate labels, and then perform reverse denoising to recover the true labels in a probabilistic sense. Unlike in conventional image generation tasks, we present the Mutual Information Guided Diffusion Model for Partial Label Learning (MDMPLL), which adapts diffusion models to weakly supervised learning and incorporates MI estimation to strengthen the consistency between denoised labels and data features, thereby improving label disambiguation. Furthermore, Dual-Path Attention Feature Fusion (DAFF) strategy is employed to enhance data representation, enabling more effective label disambiguation. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art PLL approaches on multiple datasets.