Unsupervised domain adaptation (UDA) typically carries out knowledge transfer from a label-rich source domain to an unlabeled target domain by adversarial learning. In principle, existing UDA approaches mainly focus on the global distribution alignment between domains while ignoring the intrinsic local distribution properties. Motivated by this observation, we propose an end-to-end structure-conditioned adversarial learning scheme (SCAL) that is able to preserve the intra-class compactness during domain distribution alignment. By using local structures as structure-aware conditions, the proposed scheme is implemented in a structure-conditioned adversarial learning pipeline. The above learning procedure is iteratively performed by alternating between local structures establishment and structure-conditioned adversarial learning. Experimental results demonstrate the effectiveness of the proposed scheme in UDA scenarios.

中文翻译：

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通过结构条件对抗学习进行图像分类的无监督域自适应

无监督域自适应（UDA）通常通过对抗性学习将知识从富标签的源域转移到无标签的目标域。原则上，现有的UDA方法主要关注域之间的全局分布对齐，而忽略了固有的本地分布属性。出于这一观察的目的，我们提出了一种端到端的结构条件对抗学习方案（SCAL），该方案能够在域分布对齐期间保留类内部的紧凑性。通过使用局部结构作为结构感知条件，在结构条件对抗学习流水线中实现了所提出的方案。通过在局部结构建立和结构条件对抗性学习之间交替进行迭代地执行上述学习过程。