Fang Zhen, Lu Jie, Liu Feng, Xuan Junyu, Zhang Guangquan
IEEE Trans Neural Netw Learn Syst. 2021 Oct;32(10):4309-4322. doi: 10.1109/TNNLS.2020.3017213. Epub 2021 Oct 5.
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.
无监督域适应的目的是利用有标签（源）域中的知识，来提高模型在无标签（目标）域上的学习性能，其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闭集域适应（UCSDA），即假设源域和目标域共享相同的标签集。在本文中，我们针对一个更具挑战性但更现实的场景：无监督开集域适应（UOSDA），其中目标域具有源域中未出现的未知类别。这是第一项为开集域适应提供学习界的研究，我们通过理论上研究目标分类器在未知类别上的风险来实现这一点。所提出的学习界有一个特殊项，即开集差异，它反映了目标分类器在未知类别上的风险。此外，我们提出了一种新颖的、有理论指导的无监督开集域适应算法，称为带开集差异的分布对齐（DAOD），它基于对这个开集差异界进行正则化。在几个基准数据集上的实验表明，与文献中的现有方法相比，所提出的UOSDA方法具有优越的性能。
