Autoencoding
Autoencoding is a self-supervised learning framework that belongs to the class of representation learning methods, distinguished by its use of a neural network architecture configured to reconstruct its input data through an information bottleneck, thereby learning efficient codings of the input distribution without requiring labeled data.
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23.4K
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864
Authors
297
Institutions
Publications per year
2017–2026
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Contractive Auto-Encoders: Explicit Invariance During Feature Extraction
Salah Rifai, Pascal Vincent, Xavier Muller et al. · 2011 · 1.1K citations
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Bo Zong, Song Qi, Martin Renqiang Min et al. · 2018 · 1.1K citations
Auto-encoder based dimensionality reduction
Yasi Wang, Hongxun Yao, Sicheng Zhao · Neurocomputing · 2015 · 978 citations
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297

Chinese Academy of Sciences
Beijing, China
9 h-index
23 publications
3.6K citations
Nanyang Technological University
Singapore, Singapore
8 h-index
23 publications
7.5K citations
Google (United States)
Mountain View, United States
7 h-index
20 publications
1.5K citations
Université de Montréal
Montreal, Canada
7 h-index
22 publications
8.1K citations
Carnegie Mellon University
Pittsburgh, United States
7 h-index
13 publications
2.7K citations

Chongqing University
Chongqing, China
6 h-index
12 publications
1.5K citations

Harbin Institute of Technology
Harbin, China
5 h-index
13 publications
3.6K citations

Peking University
Beijing, China
5 h-index
12 publications
658 citations
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Repository · 55 publications · 5.4K citations
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Conference · 6 publications · 383 citations
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5 publications · 706 citations
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5 publications · 90 citations
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4 publications · 498 citations
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