Abstract
In anomaly detection using deep learning, normal models based on pretrained CNNs using only normal data have become the mainstream. This framework can only use normal data for training and discards valuable information even when abnormal data is available. In addition, PaDiM, one of the representative models in this framework, creates a normal model for each position and thus cannot consider the relationship between each pixel. In this paper, we propose a method to generate a normal model by considering the information of anomalous data and neighborhood information, and achieve an image-level AUROC: 0.984 on MVTec AD.
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