CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

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.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Anomaly Detection MVTec AD CutPaste (ensemble) Detection AUROC 96.1 # 52
Anomaly Detection MVTec AD CutPaste (Image level detector) Detection AUROC 95.2 # 59
Segmentation AUROC 88.3 # 79
Anomaly Detection MVTec AD CutPaste (Patch level detector) Segmentation AUROC 96.0 # 60

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