CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

@article{Li2021CutPasteSL,
  title={CutPaste: Self-Supervised Learning for Anomaly Detection and Localization},
  author={Chun-Liang Li and Kihyuk Sohn and Jinsung Yoon and Tomas Pfister},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={9659-9669}
}
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… 

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