Source-Free Progressive Graph Learning for Open-Set Domain Adaptation

Open-set domain adaptation (OSDA) aims to transfer knowledge from a label-rich source domain to a label-scarce target domain while addressing disturbances from irrelevant target classes not present in the source data. However, most OSDA approaches are limited due to the lack of essential theoretical...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 45; číslo 9; s. 11240 - 11255
Hlavní autori:	Luo, Yadan, Wang, Zijian, Chen, Zhuoxiao, Huang, Zi, Baktashmotlagh, Mahsa
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.09.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Action recognition Adaptation Adaptation models domain adaptation Graph neural networks Hypotheses Image classification Knowledge management Labels Machine learning open-set domain adaptation Predictive models Semantics source-free domain adaptation Subspaces Task analysis Training Uncertainty Upper bounds
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
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