Anomaly detection methods aim to identify examples that do not follow the expected behavior. For various reasons, anomaly detection is typically tackled by using unsupervised approaches that assign real-valued anomaly scores based on various …
Anomaly detection aims at detecting unexpected behaviours in the data. Because anomaly detection is usually an unsupervised task, traditional anomaly detectors learn a decision boundary by employing heuristics based on intuitions, which are hard to …
Anomaly detection attempts at finding examples that deviate from the expected behaviour. Usually, anomaly detection is tackled from an unsupervised perspective because anomalous labels are rare and difficult to acquire. However, the lack of labels …