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We consider the problem of anomaly detection with a small set of partiallylabeled anomaly examples and a large-scale unlabeled dataset. This is a commonscenario in many important applications. Existing related methods eitherexclusively fit the limited anomaly examples that typically do not span theentire set of anomalies, or proceed with unsupervised learning from theunlabeled data. We propose here instead a deep reinforcement learning-basedapproach that enables an end-to-end optimization of the detection of bothlabeled and unlabeled anomalies. This approach learns the known abnormality byautomatically interacting with an anomaly-biased simulation environment, whilecontinuously extending the learned abnormality to novel classes of anomaly(i.e., unknown anomalies) by actively exploring possible anomalies in theunlabeled data. This is achieved by jointly optimizing the exploitation of thesmall labeled anomaly data and the exploration of the rare unlabeled anomalies.Extensive experiments on 48 real-world datasets show that our modelsignificantly outperforms five state-of-the-art competing methods.

Toward Deep Supervised Anomaly Detection: Reinforcement Learning from Partially Labeled Anomaly Data