CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
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-the-art 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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Fully supervised learning methods depend on datasets meticulously labeled by experts, posing a significant financial burden.
“Fully supervised learning methods [6,7], used in deep learning, depend on datasets meticulously labeled by experts for model training. Although effective, this approach poses a significant financial burden on many facilities due to the high labor costs involved.”
From An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder (2024) · cites this paper as [7]
Both CutPaste [31] and CAVGA [42] treat anomaly detection as a classification problem, while GradCAM is used for pixel-level anomaly localization.
“Both CutPaste [31] and CAVGA [42] treat anomaly detection as a classification problem, while GradCAM is used for pixel-level anomaly localization.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [31]
CutPaste, DRAEM, and MemSeg are representative methods of unsupervised anomaly detection using data augmentation to synthesize anomaly images.
“For examples, CutPaste [31] , DRAEM [91] , MemSeg [18] are representative methods.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [31]
Many unsupervised anomaly detection works use data augmentation to synthesize anomaly images and significantly improve model performance.
“There are many unsupervised anomaly detection works that use data augmentation to synthetic anomaly images and significantly improve model performance.”
From Deep Industrial Image Anomaly Detection: A Survey (2024) · cites this paper as [31]
Existing unsupervised anomaly detection methods train separate models for different object classes.
“Despite the rapid advance of unsupervised anomaly detection, existing methods require to train separate models for different objects.”
From A Unified Model for Multi-Class Anomaly Detection (2022) · cites this paper as [26]
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Song, Y., Huang, H., Wei, Q., Liu, L., & Wei, Z. (2025) · Proceedings of the 2025 6th International Conference on Computer Information and Big Data Applications · doi:10.1145/3746709.3746772 · Cited by 1 · see citing works
- Authors
- Li, C.-L., Sohn, K., Yoon, J., & Pfister, T.
- Affiliations
- Google (United States) (US)
- Venue
- 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2021-06
- Pages
- 9659-9669
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W3166166117
- Cited by
- 1,107
- References
- 57
- Citations by year
- 2026: 211 · 2025: 304 · 2024: 277 · 2023: 198 · 2022: 108 · 2021: 9
- FWCI
- 202.89
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- anomaly detection, anomaly localization, self-supervised learning, one-class classification, defect detection, image patches, transfer learning
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