Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning

TL;DR

A novel Class-specific Augmentation based Disentanglement (CAD) framework is proposed, which tackles instance entanglement by both intra- and inter-class regulations, and improves the clarity of class boundaries and reduces class confusion caused by entanglement.

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.

Figures (11)

• Figure 1: Illustration of how solely reducing intra-class distance may lead to class confusion (Figure (a)) and how class-specific augmentation combined with confidence-based penalties can mitigate confusion by increasing inter-class distance (Figure (b)). In Figure (a), similar instances with shared candidate labels may be misaligned. In Figure (b), class-specific augmentations are aligned while simultaneously lowering the confidence of confusing labels.
• Figure 1: T-SNE visualization on the Fashion-MNIST benchmark, with different colors representing different classes.
• Figure 2: Illustration of CAD framework. The upper part forces the model to produce outputs with less ambiguity based on weighted loss. The lower part generates class-specific augmentations that amplify specific features and guide them to have aligned representations. "//" denotes where gradient propagation is stopped. In the figure, "Q1-Dog" and "K1-Dog" represent the query and key embeddings for dog class-specific augmentations of img1, respectively. Here, augmentations guided by the same label are represented using the same color.
• Figure 2: Confusion Matrix on the Fashion-MNIST benchmark.
• Figure 3: Training accuracy on entangled instances under different similarity thresholds. "0" indicates that all entangled instances are used, reflecting the original accuracy. All models are trained on the full training set, and the entangled subsets are used for evaluation.