Unsupervised domain adaptation aims to learn a task classifier that performs well on the unlabeled target domain, by utilizing the labeled source domain. Inspiring results have been acquired by learning domain-invariant deep features via domain-adversarial training. However, its parallel design of task and domain classifiers limits the ability to achieve a finer category-level domain alignment. To promote categorical domain adaptation (CatDA), based on a joint category-domain classifier, we propose novel losses of adversarial training at both domain and category levels. Since the joint classifier can be regarded as a concatenation of individual task classifiers respectively for the two domains, our design principle is to enforce consistency of category predictions between the two task classifiers. Moreover, we propose a concept of vicinal domains whose instances are produced by a convex combination of pairs of instances respectively from the two domains. Intuitively, alignment of the possibly infinite number of vicinal domains enhances that of original domains. We propose novel adversarial losses for vicinal domain adaptation (VicDA) based on CatDA, leading to Vicinal and Categorical Domain Adaptation (ViCatDA). We also propose Target Discriminative Structure Recovery (TDSR) to recover the intrinsic target discrimination damaged by adversarial feature alignment. We also analyze the principles underlying the ability of our key designs to align the joint distributions. Extensive experiments on several benchmark datasets demonstrate that we achieve the new state of the art.

无监督域适应旨在通过利用标记的源域来学习在未标记的目标域上表现良好的任务分类器。通过通过领域对抗训练学习领域不变的深度特征，已经获得了令人鼓舞的结果。但是，其任务和域分类器的并行设计限制了实现更好的类别级别域对齐的能力。促进分类领域适应（CatDA），基于联合的类别-领域分类器，我们提出了领域和类别级别的对抗训练的新损失。由于联合分类器可以分别视为两个领域的单个任务分类器的串联，因此我们的设计原则是在两个任务分类器之间强制类别预测的一致性。此外，我们提出了邻域的概念，其实例是由分别来自两个域的实例对的凸组合产生的。直观上，可能无限数量的相邻域的对齐增强了原始域的对齐。我们提出基于CatDA的邻近域适应（VicDA）的新型对抗性损失，从而导致邻近域和类别域适应（ViCatDA）。我们还提出了目标判别结构恢复（TDSR），以恢复由于对抗特征对齐而受损的内在目标判别力。我们还分析了关键设计对齐关节分布的能力的基本原理。在几个基准数据集上进行的广泛实验表明，我们达到了最新水平。