Deep Autoencoders with Value-at-Risk Thresholding for Unsupervised Anomaly Detection

IEEE International Symposium on Multimedia 2019 Many real-world monitoring and surveillance applications require non-trivial anomaly detection to be run in the streaming model. We consider an incremental-learning approach, wherein a deep-autoencoding (DAE) model of what is normal is trained and used...

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Hauptverfasser:	Akhriev, Albert, Marecek, Jakub
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Learning
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