Multi-Scale Patch-Based Representation Learning for Image Anomaly Detection and Segmentation

Unsupervised representation learning has been proven to be effective for the challenging anomaly detection and segmentation tasks. In this paper, we propose a multi-scale patch-based representation learning method to extract critical and representative information from normal images. By taking the relative feature similarity between patches of different local distances into account, we can achieve better representation learning. Moreover, we propose a refined way to improve the self-supervised learning strategy, thus allowing our model to learn better geometric relationship between neighboring patches. Through sliding patches of different scales all over an image, our model extracts representative features from each patch and compares them with those in the training set of normal images to detect the anomalous regions. Our experimental results on MVTec AD dataset and BTAD dataset demonstrate the proposed method achieves the state-of-the-art accuracy for both anomaly detection and segmentation.