Large-scale labeled training datasets have enabled deep neural networks to
excel across a wide range of benchmark vision tasks. However, in many
applications, it is prohibitively expensive and time-consuming to obtain large
quantities of labeled data. To cope with limited labeled training data, many
have attempted to directly apply models trained on a large-scale labeled source
domain to another sparsely labeled or unlabeled target domain. Unfortunately,
direct transfer across domains often performs poorly due to the presence of
domain shift or dataset bias. Domain adaptation is a machine learning paradigm
that aims to learn a model from a source domain that can perform well on a
different (but related) target domain. In this paper, we review the latest
single-source deep unsupervised domain adaptation methods focused on visual
tasks and discuss new perspectives for future research. We begin with the
definitions of different domain adaptation strategies and the descriptions of
existing benchmark datasets. We then summarize and compare different categories
of single-source unsupervised domain adaptation methods, including
discrepancy-based methods, adversarial discriminative methods, adversarial
generative methods, and self-supervision-based methods. Finally, we discuss
future research directions with challenges and possible solutions.

本文综述了最新的单源深度无监督域适应方法，主要集中在视觉任务中，并讨论了未来研究的新视角。首先介绍了不同领域适应策略的定义和现有基准数据集的描述，然后总结和比较了不同类别的单源无监督域适应方法，包括基于差异性的方法，对抗式鉴别方法，对抗式生成方法和自监督方法，并探讨了未来的研究方向、挑战和可能的解决方案。