Papers › CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Chun-Liang Li, Kihyuk Sohn, Jinsung Yoon, Tomas Pfister
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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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Anomaly Classification | GoodsAD | CutPaste | AUPR | 62.8 | #10 of 11 | Archive leaderboard | report |
| Anomaly Classification | GoodsAD | CutPaste | AUROC | 60.2 | #10 of 11 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CutPaste (ensemble) | Detection AUROC | 96.1 | #84 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CutPaste (Image level detector) | Detection AUROC | 95.2 | #92 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CutPaste (Image level detector) | Segmentation AUROC | 88.3 | #92 of 148 | Archive leaderboard | report |
| Anomaly Detection | MVTec AD | CutPaste (Patch level detector) | Segmentation AUROC | 96.0 | #137 of 148 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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