Deep learning has become the method of choice to tackle real-world problems in different domains, partly because of its ability to learn from data and achieve impressive performance on a wide range of applications. However, its success usually relies on two assumptions: (i) vast troves of labeled datasets are required for accurate model fitting, and (ii) training and testing data are independent and identically distributed. Its performance on unseen target domains, thus, is not guaranteed, especially when encountering out-of-distribution data at the adaptation stage. The performance drop on data in a target domain is a critical problem in deploying deep neural networks that are successfully trained on data in a source domain. Unsupervised domain adaptation (UDA) is proposed to counter this, by leveraging both labeled source domain data and unlabeled target domain data to carry out various tasks in the target domain. UDA has yielded promising results on natural image processing, video analysis, natural language processing, time-series data analysis, medical image analysis, etc. In this review, as a rapidly evolving topic, we provide a systematic comparison of its methods and applications. In addition, the connection of UDA with its closely related tasks, e.g., domain generalization and out-of-distribution detection, has also been discussed. Furthermore, deficiencies in current methods and possible promising directions are highlighted.

深度学习在许多领域都展现出其超凡表现，但仅仅依靠大量标记数据来训练模型并不能保证其在面临目标域的分布变化时仍有出色的表现，无监督域自适应通过利用源领域标记数据和目标领域未标记数据来解决该问题，并已在自然图像处理、自然语言处理等领域取得了许多令人期待的结果，该文对该领域的方法和应用进行了系统比较，并强调了当前方法的不足及未来研究方向。

深度无监督域自适应：近期进展和展望综述