Memorizing Normality to Detect Anomaly: Memory-Augmented Deep Autoencoder for Unsupervised Anomaly Detection

Deep autoencoder has been extensively used for anomaly detection. Training on the normal data, the autoencoder is expected to produce higher reconstruction error for the abnormal inputs than the normal ones, which is adopted as a criterion for identifying anomalies. However, this assumption does not...

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Veröffentlicht in:	Proceedings / IEEE International Conference on Computer Vision S. 1705 - 1714
Hauptverfasser:	Gong, Dong, Liu, Lingqiao, Le, Vuong, Saha, Budhaditya, Mansour, Moussa Reda, Venkatesh, Svetha, Van Den Hengel, Anton
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 01.10.2019
Schlagworte:	Anomaly detection Decoding Encoding Image reconstruction Memory modules Micromechanical devices Training
ISSN:	2380-7504
Online-Zugang:	Volltext
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