Leveraging Vector-Quantized Variational Autoencoder Inner Metrics for Anomaly Detection

Abstract

Anomaly Detection (AD) is an important research topic, with very diverse applications such as industrial defect detection, medical diagnosis, fraud detection, intrusion detection, etc. Within the last few years, deep learning-based methods have become the standard approach for AD. In many practical cases, the anomalies are unknown in advance. Therefore, most of challenging AD problems need to be addressed in an unsupervised or weakly supervised framework. In this context, deep generative models are widely used, in particular Variational Autoencoder (VAE) models. VAEs have been extended to Vector-Quantized VAEs (VQ-VAEs), a model increasingly popular because of its versatility enabled by the discrete latent space. We present for the first time a robust approach which takes advantage of the inner metrics of VQ-VAEs for AD. We show that the distance between the output of the encoder and the codebook vectors of a VQ-VAE provides a valuable information which can be used to localize the anomalies. In our approach, this metric complements a reconstruction-based metric to improve AD results. We compare our model with state-of-the-art AD models on three standards datasets, including the MVTec, UCSD-Ped1 and CIFAR-10 datasets. Experiments show that the proposed method yields high competitive results.