Domain consistency regularization for unsupervised multi-source domain adaptive classification

Domain consistency regularization for unsupervised multi-source domain adaptive classification

Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only between the source and target domains but also among multiple source domains. Most...

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Bibliografski detalji
Glavni autori: Luo, Zhipeng, Zhang, Xiaobing, Lu, Shijian, Yi, Shuai
Daljnji autori: School of Computer Science and Engineering
Format: Journal Article
Jezik:English
Izdano: 2023
Teme:
Engineering::Computer science and engineering
Domain Adaptation
Transfer Learning
Online pristup:https://hdl.handle.net/10356/164101

Internet

https://hdl.handle.net/10356/164101

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