Learning with Partial Labels from Semi-supervised Perspective

Authors

  • Ximing Li Jilin University
  • Yuanzhi Jiang Jilin University
  • Changchun Li Jilin University
  • Yiyuan Wang Northeast Normal University
  • Jihong Ouyang Jilin University

DOI:

https://doi.org/10.1609/aaai.v37i7.26043

Keywords:

ML: Semi-Supervised Learning, ML: Classification and Regression, ML: Multi-Class/Multi-Label Learning & Extreme Classification

Abstract

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://github.com/changchunli/PLSP.

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Published

2023-06-26

How to Cite

Li, X., Jiang, Y., Li, C., Wang, Y., & Ouyang, J. (2023). Learning with Partial Labels from Semi-supervised Perspective. Proceedings of the AAAI Conference on Artificial Intelligence, 37(7), 8666-8674. https://doi.org/10.1609/aaai.v37i7.26043

Issue

Section

AAAI Technical Track on Machine Learning II