Semi-supervised Labeling
Semi-Supervised Labeling is a data annotation methodology within image labeling that combines a small set of manually labeled examples with a larger set of unlabeled data to improve labeling accuracy through iterative learning, thereby reducing the dependency on extensive human-annotated datasets.
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7.7K
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608
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255
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2017–2026
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160
Hieu Pham, Zihang Dai, Qizhe Xie et al. · 2021 · 505 citations
NLNL: Negative Learning for Noisy Labels
Young‐Dong Kim, Junho Yim, Juseung Yun et al. · 2019 · 302 citations
Active co-analysis of a set of shapes
Yunhai Wang, Shmulik Asafi, Oliver van Kaick et al. · ACM Transactions on Graphics · 2012 · 264 citations
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Carnegie Mellon University
Pittsburgh, United States
6 h-index
13 publications
932 citations

Xidian University
Xi'an, China
5 h-index
10 publications
270 citations
Nanjing University
Nanjing, China
4 h-index
14 publications
714 citations

University of Edinburgh
Edinburgh, United Kingdom
4 h-index
5 publications
194 citations
Columbia University
New York, United States
3 h-index
5 publications
155 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
3 h-index
6 publications
196 citations
Stanford University
Stanford, United States
3 h-index
7 publications
385 citations
Nanyang Technological University
Singapore, Singapore
3 h-index
6 publications
149 citations
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Repository · 9 publications · 172 citations
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4 publications · 72 citations
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4 publications · 95 citations
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3 publications · 101 citations
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3 publications · 106 citations
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