AltUB: Alternating Training Method to Update Base Distribution of Normalizing Flow for Anomaly Detection

Understand

Unsupervised anomaly detection is coming into the spotlight these days in various practical domains due to the limited amount of anomaly data.

• One of the major approaches for it is a normalizing flow which pursues the invertible transformation of a complex distribution as images into an easy distribution as N(0, I).
• In fact, algorithms based on normalizing flow like FastFlow and CFLOW-AD establish state-of-the-art performance on unsupervised anomaly detection tasks.
• Nevertheless, we investigate these algorithms convert normal images into not N(0, I) as their destination, but an arbitrary normal distribution.