Residual Networks

Residual Networks is a deep neural network architecture that belongs to the class of feedforward convolutional networks and is distinguished by its use of residual blocks, which introduce skip connections to enable direct gradient propagation and facilitate the training of extremely deep models by learning residual functions with reference to the layer inputs.

2.5K

Publications

534.2K

Citations

8.8K

Authors

1.9K

Institutions

Publications per year

2017–2026

2.4K

Authors

8.8K

  1. CS
    Chunhua Shen

    The University of Adelaide, Australia

    9 publications · 4.9K citations

    9

    h-index

  2. MZ
    Md Zahangir Alom

    University of Dayton, United States

    8 publications · 1.4K citations

    8

    h-index

  3. YZ
    Yulun Zhang

    Northeastern University, United States

    8 publications · 6K citations

    8

    h-index

  4. YZ
    Yang Zhang

    University of Michigan, United States

    8 publications · 625 citations

    8

    h-index

  5. YL
    Yunsong Li

    Xidian University, China

    8 publications · 576 citations

    8

    h-index

1–5 of 8.8K

Venues

  1. arXiv (Cornell University)

    Repository · 298 publications · 46.8K citations

    78

    h-index

  2. IEEE Access

    146 publications · 9K citations

    46

    h-index

  3. 31

    h-index

  4. Remote Sensing

    56 publications · 3.2K citations

    30

    h-index

  5. Neurocomputing

    36 publications · 2K citations

    24

    h-index

1–5