Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning

TL;DR

This paper introduces a novel framework called CAD that effectively reduces instance entanglement in instance-dependent partial label learning, leading to clearer class boundaries and improved classification accuracy.

Contribution

The paper proposes a Class-specific Augmentation based Disentanglement (CAD) framework that addresses instance entanglement in ID-PLL through intra- and inter-class regulations, a novel approach in this setting.

Findings

CAD significantly reduces class confusion and improves ID-PLL accuracy.

Experimental results show CAD outperforms existing methods in mitigating entanglement.

The framework enhances class boundary clarity and robustness against ambiguous labels.

Abstract

Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scenarios, candidate labels are often influenced by instance features, leading to the emergence of instance-dependent PLL (ID-PLL), a setting that more accurately reflects this relationship. A significant challenge in ID-PLL is instance entanglement, where instances from similar classes share overlapping features and candidate labels, resulting in increased class confusion. To address this issue, we propose a novel Class-specific Augmentation based Disentanglement (CAD) framework, which tackles instance entanglement by both intra- and inter-class regulations. For intra-class regulation, CAD amplifies class-specific features to generate class-wise augmentations and aligns same-class augmentations across instances. For inter-class regulation, CAD introduces a weighted penalty loss function that applies stronger penalties to more ambiguous labels, encouraging larger inter-class distances. By jointly applying intra- and inter-class regulations, CAD improves the clarity of class boundaries and reduces class confusion caused by entanglement. Extensive experimental results demonstrate the effectiveness of CAD in mitigating the entanglement problem and enhancing ID-PLL performance. The code is available at https://github.com/RyanZhaoIc/CAD.git.