Few-shot partial multi-label learning with credible non-candidate label

Abstract

Partial multi-label learning (PML) addresses scenarios where each training sample is associated with multiple candidate labels, but only a subset are ground-truth labels. The primary difficulty in PML is to mitigate the negative impact of noisy labels. Most existing PML methods rely on sufficient samples to train a noise-robust multi-label classifier. However, in practical scenarios, such as privacy-sensitive domains or those with limited data, only a few training samples are typically available for the target task. In this paper, we propose an approach called FsPML-CNL (Few-shot Partial Multi-label Learning with Credible Non-candidate Label) to tackle the PML problem with few-shot training samples. Specifically, FsPML-CNL first utilizes the sample features and feature-prototype similarity in the embedding space to disambiguate candidate labels and to obtain label prototypes. Then, the credible non-candidate label is selected based on label correlation and confidence, and its prototype is incorporated into the training samples to generate new data for boosting supervised information. The noise-tolerant multi-label classifier is finally induced with the original and generated samples, along with the confidence-guided loss. Extensive experiments on public datasets demonstrate that FsPML-CNL outperforms competitive baselines across different settings.