Maximum Density Divergence for Domain Adaptation

Unsupervised domain adaptation addresses the problem of transferring knowledge from a well-labeled source domain to an unlabeled target domain where the two domains have distinctive data distributions. Thus, the essence of domain adaptation is to mitigate the distribution divergence between the two...

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Vydané v:	IEEE transactions on pattern analysis and machine intelligence Ročník 43; číslo 11; s. 3918 - 3930
Hlavní autori:	Li, Jingjing, Chen, Erpeng, Ding, Zhengming, Zhu, Lei, Lu, Ke, Shen, Heng Tao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.11.2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptation Adaptation models adversarial learning Benchmark testing Density Domain adaptation Domains Empirical analysis Games Kernel Learning Measurement Performance evaluation Task analysis Training transfer learning
ISSN:	0162-8828, 1939-3539, 2160-9292, 1939-3539
On-line prístup:	Získať plný text
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