CutPaste: Self-Supervised Learning for Anomaly Detection and
Localization
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 …