Knowledge Distillation
Knowledge Distillation is a model compression technique in which a smaller, more efficient neural network (the student) is trained to emulate the behavior of a larger, more complex network (the teacher) by transferring learned representations or output distributions, thereby preserving performance while reducing computational demands for deployment in resource-constrained edge environments.
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2017–2026
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Distilling the Knowledge in a Neural Network
Geoffrey E. Hinton, Oriol Vinyals · arXiv (Cornell University) · 2015 · 14K citations · Full text
Knowledge Distillation: A Survey
Jianping Gou, Baosheng Yu, Stephen J. Maybank et al. · International Journal of Computer Vision · 2021 · 3.5K citations · Full text
Computing, Chemical Engineering, Knowledge Representation +2
FitNets: Hints for Thin Deep Nets
Adriana Romero, Nicolas Ballas, Samira Ebrahimi Kahou et al. · arXiv (Cornell University) · 2014 · 2K citations · Full text
Education, Educational Technology, Artificial Intelligence In Education +8
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981

Chinese Academy of Sciences
Beijing, China
36 h-index
234 publications
13.7K citations
Tsinghua University
Beijing, China
31 h-index
140 publications
12.3K citations

Zhejiang University
Hangzhou, China
30 h-index
183 publications
14.7K citations
University of Chinese Academy of Sciences
Beijing, China
29 h-index
155 publications
7.6K citations
University of Science and Technology of China
Hefei, China
26 h-index
84 publications
4.2K citations

Shanghai Jiao Tong University
Shanghai, China
26 h-index
125 publications
9.6K citations
Nanyang Technological University
Singapore, Singapore
23 h-index
62 publications
6K citations

Beihang University
Beijing, China
23 h-index
80 publications
4.8K citations
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Repository · 302 publications · 35.6K citations
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Conference · 79 publications · 5.5K citations
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Conference · 38 publications · 5.3K citations
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TPAMI · 25 publications · 3.1K citations
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26 publications · 1.2K citations
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