Representation Transfer
Representation Transfer is a machine learning paradigm in which knowledge in the form of feature representations, acquired from a source task or domain, is adapted and applied to improve performance in a target task or domain, thereby enabling more efficient and effective learning when data or annotations are limited.
268
Publications
31.2K
Citations
876
Authors
294
Institutions
Publications per year
2017–2026
203
Parents
268
How transferable are features in deep neural networks?
Jason Yosinski, Jeff Clune, Yoshua Bengio et al. · arXiv (Cornell University) · 2014 · 3.5K citations · Full text
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Maxime Oquab, Léon Bottou, Ivan Laptev et al. · 2014 · 3.2K citations
Do Better ImageNet Models Transfer Better?
Simon Kornblith, Jonathon Shlens, Quoc V. Le · 2019 · 1.3K citations
1–5 of 268
876
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5
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Hong Kong University of Science and Technology, Hong Kong SAR China
5 publications · 1.1K citations
5
h-index
1–5 of 876
294
Google (United States)
Mountain View, United States
13 h-index
28 publications
8.4K citations
University of California, Berkeley
Berkeley, United States
12 h-index
31 publications
9.4K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
24 publications
1.4K citations
Tsinghua University
Beijing, China
8 h-index
30 publications
4.5K citations
Massachusetts Institute of Technology
Cambridge, United States
7 h-index
12 publications
945 citations

Peking University
Beijing, China
7 h-index
20 publications
3.6K citations

KTH Royal Institute of Technology
Stockholm, Sweden
6 h-index
21 publications
4.1K citations
Korea Advanced Institute of Science and Technology
Daejeon, South Korea
5 h-index
12 publications
5.9K citations
1–8 of 294
Repository · 64 publications · 8.5K citations
29
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Conference · 6 publications · 547 citations
6
h-index
5 publications · 463 citations
5
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TPAMI · 5 publications · 762 citations
5
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Conference · 5 publications · 560 citations
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1–5