Flexible and weak supervision techniques for learning KU Leuven
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 heuristics. For instance, one can assume that anomalies fall in low-density regions and compute the negative log-likelihood as anomaly score.
Because anomaly scores are often hard to interpret, ...