The aim of unsupervised domain adaptation is to leverage the knowledge in a labeled (source) domain to improve a model’s learning performance with an unlabeled (target) domain—the basic strategy being to mitigate the effects of discrepancies between the two distributions. Most existing algorithms can only handle unsupervised closed set domain adaptation (UCSDA), i.e., where the source and target domains are assumed to share the same label set. In this article, we target a more challenging but realistic setting: unsupervised open set domain adaptation (UOSDA), where the target domain has unknown classes that are not found in the source domain. This is the first study to provide learning bound for open set domain adaptation, which we do by theoretically investigating the risk of the target classifier on unknown classes. The proposed learning bound has a special term, namely, open set difference, which reflects the risk of the target classifier on unknown classes. Furthermore, we present a novel and theoretically guided unsupervised algorithm for open set domain adaptation, called distribution alignment with open difference (DAOD), which is based on regularizing this open set difference bound. The experiments on several benchmark data sets show the superior performance of the proposed UOSDA method compared with the state-of-the-art methods in the literature.

中文翻译：

---

开放集域适应：理论界和算法

无监督域适应的目的是利用标记（源）域中的知识来提高模型在未标记（目标）域中的学习性能——基本策略是减轻两个分布之间差异的影响。大多数现有算法只能处理无监督的封闭集域自适应（UCSDA），即假设源域和目标域共享相同的标签集。在本文中，我们针对更具挑战性但更现实的设置：无监督开放集域适应 (UOSDA)，其中目标域具有源域中未找到的未知类。这是第一个为开放集域适应提供学习边界的研究，我们通过理论上研究目标分类器对未知类的风险来完成。提出的学习界有一个特殊术语，即开集差，它反映了目标分类器对未知类的风险。此外，我们提出了一种新颖的、理论指导的无监督算法，用于开放集域适应，称为具有开差（DAOD）的分布对齐，这是基于正则化这个开集差界。在几个基准数据集上的实验表明，与文献中的最先进方法相比，所提出的 UOSDA 方法具有优越的性能。