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Generalizing to Unseen Domains: A Survey on Domain Generalization

文献类型：期刊论文

作者Wang, Jindong5; Lan, Cuiling5; Liu, Chang5; Ouyang, Yidong2; Qin, Tao5; Lu, Wang3; Chen, Yiqiang3; Zeng, Wenjun5; Yu, Philip S.1,4
刊名IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
出版日期2023-08-01
卷号35期号:8页码:8052-8072
ISSN号1041-4347
关键词Domain generalization domain adaptation transfer learning out-of-distribution generalization
DOI10.1109/TKDE.2022.3178128
英文摘要Machine learning systems generally assume that the training and testing distributions are the same. To this end, a key requirement is to develop models that can generalize to unseen distributions. Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increasing interests in recent years. Domain generalization deals with a challenging setting where one or several different but related domain(s) are given, and the goal is to learn a model that can generalize to an unseen test domain. Great progress has been made in the area of domain generalization for years. This paper presents the first review of recent advances in this area. First, we provide a formal definition of domain generalization and discuss several related fields. We then thoroughly review the theories related to domain generalization and carefully analyze the theory behind generalization. We categorize recent algorithms into three classes: data manipulation, representation learning, and learning strategy, and present several popular algorithms in detail for each category. Third, we introduce the commonly used datasets, applications, and our open-sourced codebase for fair evaluation. Finally, we summarize existing literature and present some potential research topics for the future.
资助项目NSFC[61972383] ; NSF[III-1763325] ; NSF[III-1909323] ; NSF[III-2106758] ; NSF[SaTC-1930941]
WOS研究方向Computer Science ; Engineering
语种英语
出版者IEEE COMPUTER SOC
WOS记录号WOS:001033571000032
源URL[http://119.78.100.204/handle/2XEOYT63/21325]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Jindong
作者单位1.Tsinghua Univ, Inst Data Sci, Beijing 100190, Peoples R China
2.Chinese Univ Hong Kong, Sch Data Sci, Shenzhen 518172, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China
4.Univ Illinois, Chicago, IL 60607 USA
5.Microsoft Res Asia, Beijing 100080, Peoples R China
推荐引用方式
GB/T 7714		 Wang, Jindong,Lan, Cuiling,Liu, Chang,et al. Generalizing to Unseen Domains: A Survey on Domain Generalization[J]. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,2023,35(8):8052-8072.
APA Wang, Jindong.,Lan, Cuiling.,Liu, Chang.,Ouyang, Yidong.,Qin, Tao.,...&Yu, Philip S..(2023).Generalizing to Unseen Domains: A Survey on Domain Generalization.IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING,35(8),8052-8072.
MLA Wang, Jindong,et al."Generalizing to Unseen Domains: A Survey on Domain Generalization".IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 35.8(2023):8052-8072.

入库方式： OAI收割

来源：计算技术研究所

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