🤖 AI Summary
This work addresses the limitation of pseudo-label quality in semi-supervised image classification by proposing a contrastive learning framework integrated with a distribution matching mechanism. For the first time in semi-supervised contrastive learning, the method explicitly aligns the feature distributions of labeled and unlabeled data by minimizing the divergence in their statistical characteristics within the embedding space, thereby enhancing the reliability of pseudo-labels and the model’s generalization capability. Extensive experiments on multiple standard image classification benchmarks demonstrate that the proposed approach significantly outperforms existing state-of-the-art methods, confirming the effectiveness and novelty of incorporating feature distribution alignment to improve semi-supervised learning performance.
📝 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.