将分布匹配整合到半监督对比学习中以处理标记和未标记数据
Integrating Distribution Matching into Semi-Supervised Contrastive Learning for Labeled and Unlabeled Data
AI总结：
本研究通过整合分布匹配技术，提升半监督对比学习中伪标签的利用效率，以提高图像分类性能。
摘要：
深度学习的发展极大地提高了监督图像分类的性能。然而，标记数据的成本较高，推动了对无监督学习方法如对比学习的研究。在现实场景中，完全未标记的数据集很少，因此在少量标记数据与大量未标记数据共存的情况下，半监督学习（SSL）变得尤为重要。一种著名的半监督对比学习方法是为未标记数据分配伪标签。本研究旨在通过在标记和未标记特征嵌入之间引入分布匹配，增强基于伪标签的SSL，以提高多个数据集上的图像分类准确性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
