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.

1.6K

Publications

118.3K

Citations

6.1K

Authors

981

Institutions

Publications per year

2017–2026

1.6K

Authors

6.1K

  1. DT
    Dacheng Tao

    The University of Sydney, Australia

    19 publications · 4.7K citations

    17

    h-index

  2. MS
    Mingli Song

    Zhejiang University, China

    14 publications · 912 citations

    12

    h-index

  3. XW
    Xinchao Wang

    National University of Singapore, Singapore

    12 publications · 716 citations

    11

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  4. CX
    Chang Xu

    The University of Sydney, Australia

    9 publications · 569 citations

    9

    h-index

  5. YW
    Yunhe Wang

    The University of Sydney, Australia

    9 publications · 554 citations

    9

    h-index

1–5 of 6.1K

Venues

  1. arXiv (Cornell University)

    Repository · 302 publications · 35.6K citations

    60

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 79 publications · 5.5K citations

    39

    h-index

  3. 32

    h-index

  4. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 25 publications · 3.1K citations

    23

    h-index

  5. 22

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1–5