Unsupervised domain adaptation (UDA) and semi-supervised learning (SSL) are two typical strategies to reduce expensive manual annotations in machine learning. In order to learn effective models for a target task, UDA utilizes the available labeled source data, which may have different distributions from unlabeled samples in the target domain, while SSL employs few manually annotated target samples. Although UDA and SSL are seemingly very different strategies, we find that they are closely related in terms of task objectives and solutions, and SSL is a special case of UDA problems. Based on this finding, we further investigate whether SSL methods work on UDA tasks. By adapting eight representative SSL algorithms on UDA benchmarks, we show that SSL methods are strong UDA learners. Especially, state-of-the-art SSL methods significantly outperform existing UDA methods on the challenging UDA benchmark of DomainNet, and state-of-the-art UDA methods could be further enhanced with SSL techniques. We thus promote that SSL methods should be employed as baselines in future UDA studies and expect that the revealed relationship between UDA and SSL could shed light on future UDA development. Codes are available at \url{https://github.com/YBZh}.

研究发现，自适应领域不受监督学习（UDA）和半监督学习（SSL）虽然看似不同，但其任务目标和解决方案十分相似。基于这个发现，通过在UDA基准测试上适应八种SSL算法，发现SSL方法具有较强的UDA学习能力，在DomainNet挑战性UDA基准测试上，SSL方法显着优于现有的UDA方法，并且可以通过SSL技术进一步增强现有UDA方法的性能。因此，未来UDA研究中应将SSL方法作为基线应用，并期望揭示的UDA和SSL之间的关系能够给未来UDA发展带来启示。

半监督模型是强大的无监督域自适应学习器