  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.
