Transfer Learning for Anomaly Detection through Localized and Unsupervised Instance Selection KU Leuven
Anomaly detection attempts to identify instances that deviate from expected behavior. Constructing performant anomaly detectors on real-world problems often requires some labeled data, which can be difficult and costly to obtain. However, often one considers multiple, related anomaly detection tasks. Therefore, it may be possible to transfer labeled instances from a related anomaly detection task to the problem at hand. This paper proposes a ...