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
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2017–2026
54
Variants
Denoising Autoencoder
Parents
70
Image Denoising and Inpainting with Deep Neural Networks
Junyuan Xie, Linli Xu, Enhong Chen · 2012 · 1.2K citations
Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Salah Rifai, Pascal Vincent, Xavier Muller et al. · 2011 · 1.1K citations
Stacked Convolutional Denoising Auto-Encoders for Feature Representation
Bo Du, Wei Xiong, Jia Wu et al. · IEEE Transactions on Cybernetics · 2016 · 421 citations
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Université de Montréal
Montreal, Canada
5 h-index
17 publications
7.2K citations
Nanchang University
Nanchang, China
4 h-index
14 publications
522 citations
Shenzhen Institutes of Advanced Technology
Shenzhen, China
4 h-index
6 publications
309 citations
Wuhan University
Wuhan, China
3 h-index
6 publications
1.7K citations

Shanghai Jiao Tong University
Shanghai, China
3 h-index
4 publications
99 citations

Chinese Academy of Sciences
Beijing, China
3 h-index
5 publications
275 citations
Research Center for Information Technology Innovation, Academia Sinica
Taipei, Taiwan
3 h-index
9 publications
287 citations
University of Bern
Bern, Switzerland
2 h-index
4 publications
146 citations
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Repository · 13 publications · 455 citations
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2 publications · 62 citations
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Conference · 2 publications · 25 citations
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1 publications · 35 citations
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