A Two-class Hyper-spherical Autoencoder for Supervised Anomaly Detection

Abstract

Supervised anomaly detection has been a tough problem due to its necessity of special handling of unseen anomalies. In this paper, we present a heuristic implementation of variational auto-encoder with von-Mises Fisher prior applied to a supervised anomaly detector. The closed latent space like sphere is suitable for detecting unseen anomalies because we have a possibility to "fill" the space with seen training samples. If it ideally works, the reconstruction error will be high for all unseen anomalies. Experiments show that our model can separate normal and anomaly samples in the spherical latent space. It is also shown that he proposed model improves the performance for seen anomalies without degrading the performance for unseen anomalies.