Unsupervised Transfer Learning
Unsupervised Transfer Learning is a transfer learning paradigm in which knowledge is transferred from a source domain to a target domain without relying on labeled data in the target domain, distinguishing itself from supervised and semi-supervised transfer learning by the absence of label supervision during adaptation.
629
Publications
53.3K
Citations
2.1K
Authors
660
Institutions
Publications per year
2017–2026
574
Parents
629
Hoo-Chang Shin, Holger R. Roth, Mingchen Gao et al. · IEEE Transactions on Medical Imaging · 2016 · 5.8K citations · Full text
Moment Matching for Multi-Source Domain Adaptation
Xingchao Peng, Qinxun Bai, Xide Xia et al. · 2019 · 1.6K citations
Autoencoders, Unsupervised Learning, and Deep Architectures
Pierre Baldi · 2011 · 1.1K citations
1–5 of 629
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Carnegie Mellon University
Pittsburgh, United States
17 h-index
37 publications
4.8K citations
Tsinghua University
Beijing, China
17 h-index
47 publications
3K citations

Chinese Academy of Sciences
Beijing, China
17 h-index
38 publications
2.7K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
34 publications
2K citations

University of Electronic Science and Technology of China
Chengdu, China
14 h-index
49 publications
3.2K citations

Peking University
Beijing, China
14 h-index
40 publications
4.4K citations
University of Technology Sydney
Sydney, Australia
13 h-index
33 publications
4.1K citations

Harbin Institute of Technology
Harbin, China
13 h-index
26 publications
2.5K citations
1–8 of 660
Repository · 86 publications · 6.7K citations
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Conference · 23 publications · 1.2K citations
20
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15 publications · 780 citations
15
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15 publications · 1.1K citations
14
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TPAMI · 10 publications · 1.3K citations
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