Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
This work addresses the challenge of leveraging limited labeled data with abundant unlabeled data in real-world scenarios, representing an incremental improvement in semi-supervised learning methods.
The study tackled the problem of improving semi-supervised contrastive learning for image classification by integrating distribution matching between labeled and unlabeled feature embeddings, resulting in enhanced accuracy across multiple datasets.
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.