Semi-supervised learning (SSL) can utilize unlabeled data to enhance model performance. In recent years, with increasingly powerful generative models becoming available, a large number of synthetic images have been uploaded to public image sets. Therefore, when collecting unlabeled data from these sources, the inclusion of synthetic images is inevitable. This prompts us to consider the impact of unlabeled data mixed with real and synthetic images on SSL. In this paper, we set up a new task, Real and Synthetic hybrid SSL (RS-SSL), to investigate this problem. We discover that current SSL methods are unable to fully utilize synthetic data and are sometimes negatively affected. Then, by analyzing the issues caused by synthetic images, we propose a new SSL method, RSMatch, to tackle the RS-SSL problem. Extensive experimental results show that RSMatch can better utilize the synthetic data in unlabeled images to improve the SSL performance. The effectiveness is further verified through ablation studies and visualization.

通过分析合成图像的问题，本文提出了一种新的SSL方法RSMatch来解决混合真实和合成图像对SSL的影响问题，并通过实验证明RSMatch能够更好地利用未标记图像中的合成数据来提高SSL性能。

从障碍到机遇：通过合成数据增强半监督学习