Transfer Function Learning
Transfer Function Learning is a computational methodology that, as a specialized form of transfer learning, focuses on the systematic acquisition and adaptation of functional mappings from source domains to target domains to improve model performance under domain shift.
91
Publications
4.9K
Citations
347
Authors
169
Institutions
Publications per year
2017–2026
69
Parents
91
Unsupervised Cross-Domain Image Generation
Yaniv Taigman, Adam Polyak, Lior Wolf · arXiv (Cornell University) · 2016 · 430 citations · Full text
Cross-City Transfer Learning for Deep Spatio-Temporal Prediction
Leye Wang, Geng Xu, Xiaojuan Ma et al. · 2019 · 151 citations · Full text
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Hong Kong University of Science and Technology, Hong Kong SAR China
2 publications · 171 citations
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Hong Kong University of Science and Technology, Hong Kong SAR China
2 publications · 171 citations
2
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Hong Kong University of Science and Technology, Hong Kong SAR China
2 publications · 171 citations
2
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1–5 of 347
169
Tsinghua University
Beijing, China
4 h-index
12 publications
2.6K citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
3 h-index
11 publications
844 citations

Chongqing University
Chongqing, China
3 h-index
9 publications
590 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
11 publications
523 citations

Hong Kong Polytechnic University
Hong Kong, Hong Kong
2 h-index
3 publications
68 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
2 h-index
8 publications
684 citations

Southwest Jiaotong University
Chengdu, China
2 h-index
9 publications
604 citations
Jilin University
Changchun, China
2 h-index
8 publications
342 citations
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Repository · 4 publications · 482 citations
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3 publications · 230 citations
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3 publications · 187 citations
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3 publications · 108 citations
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3 publications · 112 citations
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