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.

通过使用VQ-VAEs结合密度和基于恢复的方法，我们提出一种识别实现无监督异常检测的新方法，该方法通过对图像进行编码将概率分布建模，并将不常见的编码替换为从先验分布中采样的编码集合, 通过对生成的恢复图像和原始图像之间的平均L1距离度量像素级的异常得分，我们对MOOD挑战数据集进行了测试，相比具有VAEs的标准重建方法，我们的方法具有更高的准确性。

利用向量量化变分自编码器恢复潜空间进行异常检测