  We aim at constructing a high performance model for defect detection that
detects unknown anomalous patterns of an image without anomalous data. To this
end, we propose a two-stage framework for building anomaly detectors using
normal training data only. We first learn self-supervised deep representations
and then build a generative one-class classifier on learned representations. We
learn representations by classifying normal data from the CutPaste, a simple
data augmentation strategy that cuts an image patch and pastes at a random
location of a large image. Our empirical study on MVTec anomaly detection
dataset demonstrates the proposed algorithm is general to be able to detect
various types of real-world defects. We bring the improvement upon previous
arts by 3.1 AUCs when learning representations from scratch. By transfer
learning on pretrained representations on ImageNet, we achieve a new
state-of-theart 96.6 AUC. Lastly, we extend the framework to learn and extract
representations from patches to allow localizing defective areas without
annotations during training.
