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Mainstream unsupervised anomaly detection algorithms often excel in academic datasets, yet their real-world performance is restricted due to the controlled experimental conditions involving clean training data.
S5-a labeled anomaly detection dataset, version 1.0 (16m), 2015
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Julien Audibert, Pietro Michiardi, Frédéric Guyard, Sébastien Marti, and Maria A Zuluaga · 2020
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Ailin Deng and Bryan Hooi · 2021
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Multivariate time series anomaly detection and interpretation using hierarchical inter-metric and temporal embedding
Zhihan Li, Youjian Zhao, Jiaqi Han, Ya Su, Rui Jiao, Xidao Wen, and Dan Pei · 2021
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Diffusion models for medical anomaly detection
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Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress
Renjie Wu and Eamonn Keogh · 2021
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Chris U Carmona, Francois-Xavier Aubet, Valentin Flunkert, and Jan Gasthaus · 2022
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