Universal Domain Adaptation (UniDA) aims to transfer knowledge from a source domain to a target domain without any constraints on label sets. Since both domains may hold private classes, identifying target common samples for domain alignment is an essential issue in UniDA. Most existing methods require manually specified or hand-tuned threshold values to detect common samples thus they are hard to extend to more realistic UniDA because of the diverse ratios of common classes. Moreover, they cannot recognize different categories among target-private samples as these private samples are treated as a whole. In this paper, we propose to use Optimal Transport (OT) to handle these issues under a unified framework, namely UniOT. First, an OT-based partial alignment with adaptive filling is designed to detect common classes without any predefined threshold values for realistic UniDA. It can automatically discover the intrinsic difference between common and private classes based on the statistical information of the assignment matrix obtained from OT. Second, we propose an OT-based target representation learning that encourages both global discrimination and local consistency of samples to avoid the over-reliance on the source. Notably, UniOT is the first method with the capability to automatically discover and recognize private categories in the target domain for UniDA. Accordingly, we introduce a new metric H^3-score to evaluate the performance in terms of both accuracy of common samples and clustering performance of private ones. Extensive experiments clearly demonstrate the advantages of UniOT over a wide range of state-of-the-art methods in UniDA.
翻译:通用域适应(UniDA)旨在将知识从源域转移到目标域,不对标签组设置设置任何限制;由于这两个域可保留私人类别,确定目标通用样本以进行域对齐是UniDA的一个基本问题。大多数现有方法要求人工指定或手调临界值以探测共同样本,因此很难推广到更现实的UniDA(UniDA),因为共同类别的比例不同,它们无法在目标-私人样本中识别不同类别,因为这些私人样本被作为一个整体处理。在本文件中,我们提议利用最佳运输(OT)在一个统一的框架内处理这些问题,即UNOT。首先,基于OT的部分调整适应填充方式的设计是为了探测共同类别,而没有为符合实际的UnidDA设定任何预先确定的临界值。它可以自动发现共同和私人类别之间的内在差异,因为从OTATA获得的任务矩阵的统计信息有不同。第二,我们提议以OT代表为基础进行指标学习,既鼓励全球歧视,也鼓励地方样本的一致性以避免过度依赖来源。值得注意的是,Uniot(OIT)是第一个在目标域域内自动发现和明确确认私人类别私域域内对UDA的绩效进行试验的能力的方法。