ClusMatch: Improving Deep Clustering by Unified Positive and Negative Pseudo-label Learning.

Authors

Jianlong Wu, Zihan Li, Wei Sun, Jianhua Yin, Liqiang Nie, Zhouchen Lin

Published in

IEEE transactions on pattern analysis and machine intelligence. Volume PP. Jul 15, 2025. Epub Jul 15, 2025.

Abstract

Recently, deep clustering methods have achieved remarkable results compared to traditional clustering approaches. However, its performance remains constrained by the absence of annotations. A thought-provoking observation is that there is still a significant gap between deep clustering and semi-supervised classification methods. Even with only a few labeled samples, the accuracy of semi-supervised learning is much higher than that of clustering. Given that we can annotate a small number of samples in a certain unsupervised way, the clustering task can be naturally transformed into a semi-supervised setting, thereby achieving comparable performance. Based on this intuition, we propose ClusMatch, a unified positive and negative pseudo-label learning based semi-supervised learning framework, which is pluggable and can be applied to existing deep clustering methods. Specifically, we first leverage the pre-trained deep clustering network to compute predictions for all samples, and then design specialized selection strategies to pick out a few high-quality samples as labeled samples for supervised learning. For the unselected samples, the novel unified positive and negative pseudo-label learning is introduced to provide additional supervised signals for semi-supervised fine-tuning. We also propose an adaptive positive-negative threshold learning strategy to further enhance the confidence of generated pseudo-labels. Extensive experiments on six widely-used datasets and one large-scale dataset demonstrate the superiority of our proposed ClusMatch. For example, ClusMatch achieves a significant accuracy improvement of 5.4% over the state-of-the-art method ProPos on an average of these six datasets. Source code can be found at https://github.com/XY-ATOE/ClusMatch.

PMID:
40663672
Bibliographic data and abstract were imported from PubMed on 16 Jul 2025.

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