Despite the progress made in domain adaptation, solving Unsupervised Domain Adaptation (UDA) problems with a general method under complex conditions caused by label shifts between domains remains a formidable task. In this work, we comprehensively investigate four distinct UDA settings including closed set domain adaptation, partial domain adaptation, open set domain adaptation, and universal domain adaptation, where shared common classes between source and target domains coexist alongside domain-specific private classes. The prominent challenges inherent in diverse UDA settings center around the discrimination of common/private classes and the precise measurement of domain discrepancy. To surmount these challenges effectively, we propose a novel yet effective method called Learning Instance Weighting for Unsupervised Domain Adaptation (LIWUDA), which caters to various UDA settings. Specifically, the proposed LIWUDA method constructs a weight network to assign weights to each instance based on its probability of belonging to common classes, and designs Weighted Optimal Transport (WOT) for domain alignment by leveraging instance weights. Additionally, the proposed LIWUDA method devises a Separate and Align (SA) loss to separate instances with low similarities and align instances with high similarities. To guide the learning of the weight network, Intra-domain Optimal Transport (IOT) is proposed to enforce the weights of instances in common classes to follow a uniform distribution. Through the integration of those three components, the proposed LIWUDA method demonstrates its capability to address all four UDA settings in a unified manner. Experimental evaluations conducted on three benchmark datasets substantiate the effectiveness of the proposed LIWUDA method.

翻译：尽管域自适应领域取得了进展，但在域间标签偏移导致的复杂条件下，以通用方法解决无监督域自适应（UDA）问题仍是一项艰巨任务。本文全面研究了四种不同的UDA设定，包括闭集域自适应、部分域自适应、开集域自适应和通用域自适应，其中源域与目标域共享的公共类与域特定的私有类并存。不同UDA设定中面临的突出挑战主要围绕公共/私有类的区分以及域差异的精确度量。为有效应对这些挑战，我们提出了一种新颖且有效的方法——学习实例加权的无监督域自适应（LIWUDA），该方法适用于多种UDA设定。具体而言，所提出的LIWUDA方法构建了一个权重网络，根据每个实例属于公共类的概率为其分配权重，并设计了加权最优传输（WOT）利用实例权重进行域对齐。此外，LIWUDA方法还设计了分离与对齐（SA）损失，用于分离低相似度的实例并对齐高相似度的实例。为指导权重网络的学习，提出了域内最优传输（IOT）以强制公共类实例的权重服从均匀分布。通过整合这三个组件，所提出的LIWUDA方法能够以统一方式应对所有四种UDA设定。在三个基准数据集上进行的实验评估证实了所提LIWUDA方法的有效性。