Continual Learning for Anomaly Detection with Variational Autoencoder

Abstract

Detecting anomalies using a variational autoencoder (VAE) suffers from catastrophic forgetting when trained on a continually growing set of normal data where only the most recently added data is available. Solving this problem would allow the use of the VAE for anomaly detection in settings where it is difficult or even impossible to retain all normal data at the same time. We propose an efficient extension of a method for continual learning which alleviates catastrophic forgetting for anomaly detection using a VAE. We show on some anomaly detection problems that the definition of normal data can be continually expanded without requiring all previously seen data.