UaiNets: From Unsupervised to Active Deep Anomaly DetectionDownload PDF

27 Sept 2018 (modified: 05 May 2023)ICLR 2019 Conference Blind SubmissionReaders: Everyone
Abstract: This work presents a method for active anomaly detection which can be built upon existing deep learning solutions for unsupervised anomaly detection. We show that a prior needs to be assumed on what the anomalies are, in order to have performance guarantees in unsupervised anomaly detection. We argue that active anomaly detection has, in practice, the same cost of unsupervised anomaly detection but with the possibility of much better results. To solve this problem, we present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method, presenting results on both synthetic and real anomaly detection datasets.
Keywords: Anomaly Detection, Active Learning, Unsupervised Learning
TL;DR: A method for active anomaly detection. We present a new layer that can be attached to any deep learning model designed for unsupervised anomaly detection to transform it into an active method.
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