Deep Anomaly Detection via Active Anomaly Search

Anomaly detection (AD) holds substantial practical value, and considering the limited labeled data, the semi-supervised anomaly detection technique has garnered increasing attention. We find that previous methods suffer from insufficient exploitation of labeled data and under-exploration of unlabeled data. To tackle the above problem, we aim to search for possible anomalies in unlabeled data and use the searched anomalies to enhance performance. We innovatively model this search process as a Markov decision process and utilize a reinforcement learning algorithm to solve it. Our method, Deep Anomaly Detection and Search (DADS), integrates the exploration of unlabeled data and the exploitation of labeled data into one framework. Experimentally, we compare DADS with several state-of-the-art methods in widely used benchmarks, and the results show that DADS can efficiently search anomalies from unlabeled data and learn from them, thus achieving good performance.