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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Bibliographic Details
Main Authors: Luo, Zhipeng, Zhang, Xiaobing, Lu, Shijian, Yi, Shuai
Other Authors: School of Computer Science and Engineering
Format: Journal Article
Language:English
Published: 2023
Subjects:
Engineering::Computer science and engineering
Domain Adaptation
Transfer Learning
Online Access:https://hdl.handle.net/10356/164101

Internet

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

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