Pseudo-labeling
Pseudo-Labeling is a semi-supervised learning technique in which unlabeled data are assigned predicted labels generated by a model trained on labeled data, thereby expanding the training set with high-confidence predictions to improve model performance.
499
Publications
32.6K
Citations
2K
Authors
497
Institutions
Publications per year
2017–2026
490
Variants
Self-training
499
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Kihyuk Sohn, David Berthelot, Chunliang Li et al. · arXiv (Cornell University) · 2020 · 2.3K citations · Full text
A Survey on Deep Semi-Supervised Learning
Xiangli Yang, Zixing Song, Irwin King et al. · IEEE Transactions on Knowledge and Data Engineering · 2022 · 828 citations
Confidence Regularized Self-Training
Yang Zou, Zhiding Yu, Xiaofeng Liu et al. · 2019 · 820 citations
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497

Chinese Academy of Sciences
Beijing, China
16 h-index
45 publications
1.6K citations
Tsinghua University
Beijing, China
16 h-index
50 publications
1.9K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
14 h-index
34 publications
4.5K citations

Peking University
Beijing, China
14 h-index
42 publications
3.3K citations

Shanghai Jiao Tong University
Shanghai, China
13 h-index
43 publications
2.8K citations
University of Chinese Academy of Sciences
Beijing, China
13 h-index
45 publications
1.2K citations

Northwestern Polytechnical University
Xi'an, China
12 h-index
38 publications
1.3K citations
University of Technology Sydney
Sydney, Australia
12 h-index
38 publications
4.5K citations
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Repository · 73 publications · 5.6K citations
29
h-index
Conference · 28 publications · 3.3K citations
24
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Conference · 24 publications · 1.8K citations
21
h-index
15 publications · 875 citations
15
h-index
16 publications · 553 citations
14
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