NFAD: Fixing anomaly detection using normalizing flows

Understand

Anomaly detection is a challenging task that frequently arises in practically all areas of industry and science, from fraud detection and data quality monitoring to finding rare cases of diseases and searching for new physics.

• Most of the conventional approaches to anomaly detection, such as one-class SVM and Robust Auto-Encoder, are one-class classification methods, i.e.
• focus on separating normal data from the rest of the space.
• Such methods are based on the assumption of separability of normal and anomalous classes, and subsequently do not take into account any available samples of anomalies.