CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

• URL: http://arxiv.org/abs/2104.04015v1
• Date: Thu, 8 Apr 2021 19:04:55 GMT
• Title: CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
• Authors: Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
• Abstract summary: We propose a 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. 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.
• Score: 59.719925639875036
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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