CutPaste: Self-Supervised Learning for Anomaly Detection and Localization
Abstrak
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.
Topik & Kata Kunci
Penulis (4)
Chun-Liang Li
Kihyuk Sohn
Jinsung Yoon
Tomas Pfister
Akses Cepat
- Tahun Terbit
- 2021
- Bahasa
- en
- Total Sitasi
- 1105×
- Sumber Database
- Semantic Scholar
- DOI
- 10.1109/CVPR46437.2021.00954
- Akses
- Open Access ✓