部分域适应研究综述

域适应通过将源域知识迁移到任务相似的目标域,旨在辅助后者更好地学习.当目标域的数据标签集为源域标签的子集时,该类场景的域适应称为部分或偏域适应(partial domain adaption, PDA).相比一般的域适应,尽管PDA更具普遍性,但也更具挑战性,相关研究较少,尤其缺少系统的综述.为此,旨在弥补这一欠缺,对现有PDA方法进行全面回顾、分析和总结,为相关社区提供主题研究概貌和参考.首先针对PDA背景、概念和应用领域进行概况总结.其次,根据方法的建模特点, PDA被划分为促进正迁移和抑制负迁移两大类,进而分别作出回顾和分析.然后,对常用的实验基准数据集进行归类和总结.最后,对现有PDA工作存在的问题进行了分析,并提出了其未来可能的发展方向.

Survey on Partial Domain Adaptation

By transferring the knowledge of the source domain to the target domain with similar tasks, domain adaptation aims to assist the latter to learn better. When the data label set of the target domain is a subset of the source domain labels, the domain adaptation of this type of scenario is called partial domain adaptation (PDA). Compared with general domain adaptation, although PDA is more general, it is more challenging with few related studies, especially with the lack of systematic reviews. To fill this gap, this study conducts a comprehensive review, analysis and summary of existing PDA methods, and provides an overview and reference of subject research for the relevant community. Firstly, an overview of the PDA background, concepts, and application fields is summarized. Secondly, according to the modeling characteristics, existing PDA methods are divided into two categories: promoting positive transfer and alleviating negative transfer, and this study reviews and analyzes them respectively. Then, the commonly used experimental benchmark datasets are categorized and summarized. Finally, the problems in existing PDA studies are analyzed to point out possible future development directions.