Generalizing to Unseen Domains: A Survey on Domain Generalization
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 interest...
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| Vydáno v: | IEEE transactions on knowledge and data engineering Ročník 35; číslo 8; s. 8052 - 8072 |
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| Hlavní autoři: | , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
New York
IEEE
01.08.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 1041-4347, 1558-2191 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Zeng, Wenjun Lu, Wang Wang, Jindong Ouyang, Yidong Yu, Philip S. Qin, Tao Chen, Yiqiang Lan, Cuiling Liu, Chang |
| Author_xml | – sequence: 1 givenname: Jindong orcidid: 0000-0002-4833-0880 surname: Wang fullname: Wang, Jindong email: jindong.wang@microsoft.com organization: Microsoft Research Asia, Beijing, China – sequence: 2 givenname: Cuiling orcidid: 0000-0001-9145-9957 surname: Lan fullname: Lan, Cuiling email: culan@microsoft.com organization: Microsoft Research Asia, Beijing, China – sequence: 3 givenname: Chang surname: Liu fullname: Liu, Chang email: changliu@microsoft.com organization: Microsoft Research Asia, Beijing, China – sequence: 4 givenname: Yidong surname: Ouyang fullname: Ouyang, Yidong email: yidongouyang@link.cuhk.edu.cn organization: School of Data Science, Chinese University of Hong Kong, Shenzhen, China – sequence: 5 givenname: Tao orcidid: 0000-0002-9095-0776 surname: Qin fullname: Qin, Tao email: taoqin@microsoft.com organization: Microsoft Research Asia, Beijing, China – sequence: 6 givenname: Wang orcidid: 0000-0003-4035-0737 surname: Lu fullname: Lu, Wang email: luwang@ict.ac.cn organization: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 7 givenname: Yiqiang orcidid: 0000-0002-8407-0780 surname: Chen fullname: Chen, Yiqiang email: yqchen@ict.ac.cn organization: Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China – sequence: 8 givenname: Wenjun orcidid: 0000-0003-2531-3137 surname: Zeng fullname: Zeng, Wenjun email: wezeng@microsoft.com organization: Microsoft Research Asia, Beijing, China – sequence: 9 givenname: Philip S. orcidid: 0000-0002-3491-5968 surname: Yu fullname: Yu, Philip S. email: psyu@uic.edu organization: University of Illinois at Chicago, Chicago, IL, USA |
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| CODEN | ITKEEH |
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| Title | Generalizing to Unseen Domains: A Survey on Domain Generalization |
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