Titlebook: Machine Learning and Knowledge Discovery in Databases; European Conference, Massih-Reza Amini,Stéphane Canu,Grigorios Tsoumaka Conference p

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Detecting Anomalies with?Autoencoders on?Data Streamspresentation of normality. To this end, autoencoders are typically trained on large amounts of previously collected data before being deployed. However, in an online learning scenario, where a predictor has to operate on an evolving data stream and therefore continuously adapt to new instances, this

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Anomaly Detection via?Few-Shot Learning on?Normalityractice, however, normal data can consist of multiple classes, in which case the anomalies may appear not only outside such an enclosure but also in-between ‘normal’ classes. This paper addresses deep anomaly detection aimed at embedding ‘normal’ classes to individually close but mutually distant pr

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