Autoencoder-based Anomaly Detection
Autoencoder-Based Anomaly Detection is a method for identifying anomalous patterns in time series data by leveraging an autoencoder neural network to model normal behavior and detecting anomalies through reconstruction error deviation.
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69.6K
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3.7K
Authors
1.1K
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2017–2026
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MVTec AD — A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection
Paul Bergmann, Michael Fauser, David Sattlegger et al. · 2019 · 1.8K citations
Anomaly Detection with Robust Deep Autoencoders
Chong Zhou, Randy Paffenroth · 2017 · 1.4K citations
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University of Chinese Academy of Sciences
Beijing, China
16 h-index
44 publications
2.6K citations

Xidian University
Xi'an, China
16 h-index
65 publications
3.9K citations
Tsinghua University
Beijing, China
16 h-index
59 publications
12.3K citations

Chinese Academy of Sciences
Beijing, China
16 h-index
51 publications
3.1K citations

Shanghai Jiao Tong University
Shanghai, China
15 h-index
43 publications
3.6K citations
China University of Geosciences
Wuhan, China
13 h-index
39 publications
2.3K citations

Harbin Institute of Technology
Harbin, China
12 h-index
56 publications
2K citations

University of Electronic Science and Technology of China
Chengdu, China
12 h-index
38 publications
1.9K citations
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49 publications · 3.1K citations
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Repository · 53 publications · 2.4K citations
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35 publications · 1.6K citations
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16 publications · 506 citations
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