Semi-supervised Active Learning
Semi-Supervised Active Learning is an active learning paradigm that integrates a small set of initially labeled data with a large pool of unlabeled data, distinguishing itself from conventional active learning by leveraging both supervised learning on labeled instances and semi-supervised learning on unlabeled data to iteratively select the most informative samples for labeling, thereby improving model performance with reduced annotation effort.
271
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18K
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1K
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356
Institutions
Publications per year
2017–2026
134
Parents
271
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering · 2021 · 2.2K citations
Semi-supervised learning with graphs
Xiaojin Zhu, John Lafferty, Roni Rosenfeld · 2005 · 649 citations
A Survey on Multi-Task Learning
Yu Zhang, Qiang Yang · arXiv (Cornell University) · 2017 · 621 citations · Full text
Active Semi-Supervision for Pairwise Constrained Clustering
Sugato Basu, Arindam Banerjee, Raymond J. Mooney · 2004 · 600 citations
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356
Tsinghua University
Beijing, China
8 h-index
17 publications
804 citations

Zhejiang University
Hangzhou, China
8 h-index
25 publications
807 citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
7 h-index
12 publications
1.2K citations

Chinese Academy of Sciences
Beijing, China
6 h-index
16 publications
612 citations
Nanjing University
Nanjing, China
5 h-index
10 publications
1.1K citations
Carnegie Mellon University
Pittsburgh, United States
5 h-index
10 publications
3.3K citations
University of Science and Technology of China
Hefei, China
5 h-index
20 publications
979 citations
Wuhan University
Wuhan, China
5 h-index
21 publications
1K citations
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Repository · 16 publications · 958 citations
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7 publications · 1K citations
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5 publications · 284 citations
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5 publications · 238 citations
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4 publications · 140 citations
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