Denoising Autoencoding

Denoising Autoencoding is a self-supervised learning framework that belongs to the class of autoencoding methods, distinguished by its objective of reconstructing clean input data from a corrupted version, thereby learning robust and informative latent representations through the task of noise reduction.

70

Publications

6.1K

Citations

253

Authors

99

Institutions

Publications per year

2017–2026

54

Variants

Denoising Autoencoder

Authors

253

  1. YB
    Yoshua Bengio

    Université de Montréal, Canada

    7 publications · 1.8K citations

    7

    h-index

  2. QL
    Qiegen Liu

    Nanchang University, China

    4 publications · 159 citations

    4

    h-index

  3. SA
    Sungjin Ahn

    Mokpo National University, South Korea

    2 publications · 214 citations

    2

    h-index

  4. YT
    Yu Tsao

    Research Center for Information Technology Innovation, Academia Sinica, Taiwan

    2 publications · 54 citations

    2

    h-index

  5. CY
    Cheng Yu

    Research Center for Information Technology Innovation, Academia Sinica, Taiwan

    2 publications · 54 citations

    2

    h-index

1–5 of 253

Venues

  1. arXiv (Cornell University)

    Repository · 13 publications · 455 citations

    12

    h-index

  2. IEEE Transactions on Image Processing

    2 publications · 62 citations

    2

    h-index

  3. Neural Information Processing Systems

    Conference · 2 publications · 25 citations

    2

    h-index

  4. The Canadian Journal of Chemical Engineering

    1 publications · 35 citations

    1

    h-index

  5. 1

    h-index

1–5