Unreliable Partial Label Learning with Recursive Separation

TL;DR

This paper introduces UPLLSR, a novel two-stage framework for unreliable partial label learning that effectively separates reliable and unreliable data and progressively identifies true labels, improving performance in challenging scenarios.

Contribution

The paper proposes a recursive separation strategy and a disambiguation approach for unreliable partial label learning, addressing the limitations of previous methods under high unreliability.

Findings

Achieves state-of-the-art performance in high unreliability scenarios

Effectively separates reliable and unreliable data subsets

Progressively identifies true labels in unreliable datasets

Abstract

Partial label learning (PLL) is a typical weakly supervised learning problem in which each instance is associated with a candidate label set, and among which only one is true. However, the assumption that the ground-truth label is always among the candidate label set would be unrealistic, as the reliability of the candidate label sets in real-world applications cannot be guaranteed by annotators. Therefore, a generalized PLL named Unreliable Partial Label Learning (UPLL) is proposed, in which the true label may not be in the candidate label set. Due to the challenges posed by unreliable labeling, previous PLL methods will experience a marked decline in performance when applied to UPLL. To address the issue, we propose a two-stage framework named Unreliable Partial Label Learning with Recursive Separation (UPLLRS). In the first stage, the self-adaptive recursive separation strategy is proposed to separate the training set into a reliable subset and an unreliable subset. In the second stage, a disambiguation strategy is employed to progressively identify the ground-truth labels in the reliable subset. Simultaneously, semi-supervised learning methods are adopted to extract valuable information from the unreliable subset. Our method demonstrates state-of-the-art performance as evidenced by experimental results, particularly in situations of high unreliability. Code and supplementary materials are available at https://github.com/dhiyu/UPLLRS.