Semi-supervised Learning
Semi-Supervised Learning is a machine learning paradigm in which a predictor is trained on a combination of a relatively small labeled dataset and a large unlabeled dataset, exploiting the structure of the unlabeled data to improve learning performance relative to purely supervised or unsupervised approaches.
7.8K
Publications
573K
Citations
19.4K
Authors
3.1K
Institutions
Publications per year
2017–2026
5.1K
7.8K
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
A unified architecture for natural language processing
Ronan Collobert, Jason Weston · 2008 · 5.2K citations
Semi-Supervised Learning Literature Survey
Xiaojin Zhu · Minds at UW (University of Wisconsin) · 2005 · 3.9K citations · Full text
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23
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3.1K
Google (United States)
Mountain View, United States
63 h-index
353 publications
91.1K citations

Chinese Academy of Sciences
Beijing, China
62 h-index
632 publications
31.6K citations
Tsinghua University
Beijing, China
59 h-index
459 publications
41.4K citations
Carnegie Mellon University
Pittsburgh, United States
56 h-index
277 publications
38.5K citations
Nanjing University
Nanjing, China
52 h-index
279 publications
21.8K citations
Nanyang Technological University
Singapore, Singapore
49 h-index
230 publications
17.9K citations
University of Technology Sydney
Sydney, Australia
46 h-index
172 publications
21.1K citations
National University of Singapore
Singapore, Singapore
45 h-index
175 publications
15.1K citations
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Repository · 617 publications · 77K citations
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143 publications · 14.8K citations
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TPAMI · 96 publications · 13.9K citations
57
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118 publications · 8.4K citations
51
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150 publications · 9.2K citations
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