Generalizing to Unseen Domains: A Survey on Domain Generalization

Wang, Jindong; Lan, Cuiling; Liu, Chang; Ouyang, Yidong; Qin, Tao

doi:10.24963/ijcai.2021/628

Generalizing to Unseen Domains: A Survey on Domain Generalization

Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, Tao Qin

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Survey Track. Pages 4627-4635.

https://doi.org/10.24963/ijcai.2021/628

Keywords

Machine learning: General
Computer vision: General

Abstract

Domain generalization (DG), i.e., out-of-distribution generalization, has attracted increased 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. For years, great progress has been achieved. This paper presents the first review for recent advances in domain generalization. First, we provide a formal definition of domain generalization and discuss several related fields. Then, we categorize recent algorithms into three classes and present them in detail: data manipulation, representation learning, and learning strategy, each of which contains several popular algorithms. Third, we introduce the commonly used datasets and applications. Finally, we summarize existing literature and present some potential research topics for the future.

Jindong Wang, Cuiling Lan, Chang Liu, Yidong Ouyang, and Tao Qin. 2021. Generalizing to Unseen Domains: A Survey on Domain Generalization. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4627–4635. https://doi.org/10.24963/ijcai.2021/628

@inproceedings{ijcai2021p628,
  title     = {Generalizing to Unseen Domains: A Survey on Domain Generalization},
  author    = {Wang, Jindong and Lan, Cuiling and Liu, Chang and Ouyang, Yidong and Qin, Tao},
  booktitle = {Proceedings of the Thirtieth International Joint Conference on
               Artificial Intelligence, {IJCAI-21}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Prof. Zhi-Hua Zhou},
  pages     = {4627--4635},
  year      = {2021},
  month     = {8},
  note      = {Survey Track},
  doi       = {10.24963/ijcai.2021/628},
  url       = {https://doi.org/10.24963/ijcai.2021/628},
}

TY  - CPAPER
TI  - Generalizing to Unseen Domains: A Survey on Domain Generalization
AU  - Wang, Jindong
AU  - Lan, Cuiling
AU  - Liu, Chang
AU  - Ouyang, Yidong
AU  - Qin, Tao
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4627
EP  - 4635
DO  - 10.24963/ijcai.2021/628
UR  - https://doi.org/10.24963/ijcai.2021/628
ER  -