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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Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 35; H. 8; S. 8052 - 8072
Hauptverfasser:	Wang, Jindong, Lan, Cuiling, Liu, Chang, Ouyang, Yidong, Qin, Tao, Lu, Wang, Chen, Yiqiang, Zeng, Wenjun, Yu, Philip S.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptation models Algorithms Computational modeling Data models domain adaptation Domain generalization Machine learning Multitasking out-of-distribution generalization Predictive models Task analysis Training transfer learning
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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