Unsupervised domain adaptation for classification tasks has achieved great progress in leveraging the knowledge in a labeled (source) domain to improve the task performance in an unlabeled (target) domain by mitigating the effect of distribution discrepancy. However, most existing methods can only handle unsupervised closed set domain adaptation (UCSDA), where the source and target domains share the same label set. In this paper, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that the source domain does not have. This study is the first to give the generalization bound of open set domain adaptation through theoretically investigating the risk of the target classifier on the unknown classes. The proposed generalization bound for open set domain adaptation has a special term, namely open set difference, which reflects the risk of the target classifier on unknown classes. According to this generalization bound, we propose a novel and theoretically guided unsupervised open set domain adaptation method: Distribution Alignment with Open Difference (DAOD), which is based on the structural risk minimization principle and open set difference regularization. The experiments on several benchmark datasets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

本文提供了第一篇研究开放领域自适应的学习界限，并提出了一种基于分布对齐的新型无监督算法，该算法旨在正则化开放集差异界限，实验证明所提出的 UOSDA 方法优于现有文献中的最新方法。

开放集领域适应：理论界限与算法