Causality Inspired Representation Learning for Domain Generalization

Domain generalization (DG) is essentially an out-of-distribution problem, aiming to generalize the knowledge learned from multiple source domains to an unseen target domain. The mainstream is to leverage statistical models to model the dependence between data and labels, intending to learn represent...

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Vydané v:	Proceedings (IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Online) s. 8036 - 8046
Hlavní autori:	Lv, Fangrui, Liang, Jian, Li, Shuang, Zang, Bin, Liu, Chi Harold, Wang, Ziteng, Liu, Di
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.06.2022
Predmet:	categorization Classification algorithms Computer vision Data models Pattern recognition Representation learning retrieval; Representation learning; Self-& semi-& meta- & unsupervised learning Transfer/low-shot/long-tail learning; Machine learning; Recognition: detection
ISSN:	1063-6919
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