Semi-supervised learning (SSL) can improve model performance by leveraging unlabeled images, which can be collected from public image sources with low costs. In recent years, synthetic images have become increasingly common in public image sources due to rapid advances in generative models. Therefore, it is becoming inevitable to include existing synthetic images in the unlabeled data for SSL. How this kind of contamination will affect SSL remains unexplored. In this paper, we introduce a new task, Real-Synthetic Hybrid SSL (RS-SSL), to investigate the impact of unlabeled data contaminated by synthetic images for SSL. First, we set up a new RS-SSL benchmark to evaluate current SSL methods and found they struggled to improve by unlabeled synthetic images, sometimes even negatively affected. To this end, we propose RSMatch, a novel SSL method specifically designed to handle the challenges of RS-SSL. RSMatch effectively identifies unlabeled synthetic data and further utilizes them for improvement. Extensive experimental results show that RSMatch can transfer synthetic unlabeled data from `obstacles' to `resources.' The effectiveness is further verified through ablation studies and visualization.

本研究针对半监督学习中未标记数据被合成图像污染的问题，通过提出真实-合成混合半监督学习（RS-SSL）任务，探讨合成图像对半监督学习的影响。同时，提出RSMatch这一新方法，能有效识别和利用未标记的合成数据，将其从“障碍”转变为“资源”。研究结果表明，RSMatch显著提升了SSL模型的性能。

带有难度控制的扩散模型进行训练数据合成