Anomaly detection through latent space restoration using vector-quantized variational autoencoders

• URL: http://arxiv.org/abs/2012.06765v1
• Date: Sat, 12 Dec 2020 09:19:59 GMT
• Title: Anomaly detection through latent space restoration using vector-quantized variational autoencoders
• Authors: Sergio Naval Marimont and Giacomo Tarroni
• Abstract summary: We propose an out-of-distribution detection method using density and restoration-based approaches. 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.
• Score: 0.8122270502556374
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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