We propose an out-of-distribution detection method that combines density and
restoration-based approaches using Vector-Quantized Variational Auto-Encoders
(VQ-VAEs). The VQ-VAE model learns to encode images in a categorical latent
space. The prior distribution of latent codes is then modelled using an
Auto-Regressive (AR) model. We found that the prior probability estimated by
the AR model can be useful for unsupervised anomaly detection and enables the
estimation of both sample and pixel-wise anomaly scores. The sample-wise score
is defined as the negative log-likelihood of the latent variables above a
threshold selecting highly unlikely codes. Additionally, out-of-distribution
images are restored into in-distribution images by replacing unlikely latent
codes with samples from the prior model and decoding to pixel space. The
average L1 distance between generated restorations and original image is used
as pixel-wise anomaly score. We tested our approach on the MOOD challenge
datasets, and report higher accuracies compared to a standard
reconstruction-based approach with VAEs.