CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

Chunliang Li, Kihyuk Sohn, Jinsung Yoon et al.

2021 · 888 citations

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 anom…

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