Self-supervised Learning
Self-Supervised Learning is a machine learning paradigm in which a model generates its own supervisory signals from unlabeled data by designing pretext tasks that exploit inherent data structure, thereby enabling representation learning without explicit human annotations.
4.7K
Publications
351.1K
Citations
15.4K
Authors
2.2K
Institutions
Publications per year
2017–2026
4.3K
4.7K
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Richard Zhang, Phillip Isola, Alexei A. Efros et al. · 2018 · 12.4K citations
A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi et al. · arXiv (Cornell University) · 2020 · 7.3K citations · Full text
1–5 of 4.7K
15.4K
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18
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Korea Advanced Institute of Science and Technology, South Korea
19 publications · 2.8K citations
18
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18
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2.2K
Google (United States)
Mountain View, United States
64 h-index
529 publications
127.1K citations
Tsinghua University
Beijing, China
54 h-index
372 publications
52.5K citations

Chinese Academy of Sciences
Beijing, China
51 h-index
418 publications
33K citations
Meta (Israel)
Tel Aviv, Israel
43 h-index
213 publications
56.9K citations
Carnegie Mellon University
Pittsburgh, United States
41 h-index
213 publications
19.3K citations
University of Oxford
Oxford, United Kingdom
40 h-index
170 publications
14.9K citations
University of California, Berkeley
Berkeley, United States
40 h-index
191 publications
60.8K citations
University of Chinese Academy of Sciences
Beijing, China
40 h-index
282 publications
20.5K citations
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Repository · 891 publications · 60.8K citations
101
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Conference · 130 publications · 13.8K citations
58
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Conference · 102 publications · 15.1K citations
54
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Conference · 141 publications · 12.3K citations
49
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84 publications · 5.6K citations
42
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