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arXiv · 2601.04518

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

Shogo Nakayama·Masahiro Okuda
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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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Shogo Nakayama, Masahiro Okuda. 2026-01-08. Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data. https://doi.org/10.1109/itc-cscc66376.2025.11137694

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