Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data

• URL: http://arxiv.org/abs/2601.04518v1
• Date: Thu, 08 Jan 2026 02:32:12 GMT
• Title: Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
• Authors: Shogo Nakayama, Masahiro Okuda,
• Abstract summary: Semi-supervised contrastive learning (SSL) is highly relevant in scenarios where a small amount of labeled data coexists with a large volume of unlabeled data.<n>This study aims to enhance pseudo-label-based SSL by incorporating distribution matching between labeled and unlabeled feature embeddings.
• Score: 1.0312968200748116
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The advancement of deep learning has greatly improved supervised image classification. However, labeling data is costly, prompting research into unsupervised learning methods such as contrastive learning. In real-world scenarios, fully unlabeled datasets are rare, making semi-supervised learning (SSL) highly relevant in scenarios where a small amount of labeled data coexists with a large volume of unlabeled data. A well-known semi-supervised contrastive learning approach involves assigning pseudo-labels to unlabeled data. This study aims to enhance pseudo-label-based SSL by incorporating distribution matching between labeled and unlabeled feature embeddings to improve image classification accuracy across multiple datasets.

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