Unsupervised Anomaly Detection Using Variational Autoencoder with Gaussian Random Field Prior

Abstract

We propose a new model of Variational Autoencoder (VAE) for Anomaly Detection (AD) with improved modeling power. More precisely, we introduce a VAE model with a Gaussian Random Field (GRF) prior, namely VAE-GRF, which generalizes the classical VAE model. We show that, under some assumptions, the VAE-GRF largely outperforms the traditional VAE and some other probabilistic models developed for AD. Our experimental results suggest that the VAE-GRF could be used as a relevant VAE baseline in place of the traditional VAE with very limited additional computational cost. We provide competitive results on the public MVTec benchmark dataset for visual inspection, as well as on the public Livestock dataset dedicated to the task of unsupervised animal detection from aerial images.