Unsupervised Anomaly Detection
Unsupervised Anomaly Detection is a methodological framework within anomaly detection that identifies rare or deviant patterns in data without relying on labeled instances, distinguishing itself from supervised and semi-supervised approaches by exclusively leveraging the intrinsic structure of unlabeled datasets to model normal behavior and flag outliers.
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Citations
6.9K
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1.4K
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Publications per year
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
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
Bo Zong, Song Qi, Martin Renqiang Min et al. · 2018 · 1.1K citations
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Tsinghua University
Beijing, China
21 h-index
106 publications
16.3K citations

Chinese Academy of Sciences
Beijing, China
20 h-index
110 publications
7.6K citations

National University of Defense Technology
Changsha, China
15 h-index
78 publications
3.5K citations

Harbin Institute of Technology
Harbin, China
15 h-index
53 publications
3.1K citations
Fudan University
Shanghai, China
14 h-index
29 publications
1.2K citations

Centre National de la Recherche Scientifique
Paris, France
14 h-index
166 publications
5.8K citations
University of Chinese Academy of Sciences
Beijing, China
14 h-index
56 publications
3.8K citations

Shanghai Jiao Tong University
Shanghai, China
13 h-index
70 publications
2.7K citations
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Repository · 90 publications · 3.7K citations
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21 publications · 803 citations
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17 publications · 2K citations
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Conference · 18 publications · 2.1K citations
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