CutPaste: Self-Supervised Learning for Anomaly Detection and Localization

  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)},
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… 

Just Noticeable Learning for Unsupervised Anomaly Localization and Detection

  • Ying Zhao
  • Computer Science
    2022 IEEE International Conference on Multimedia and Expo (ICME)
  • 2022
A Just Noticeable learning for unsupervised anomaly Localization and Detection (JNLD) that embeds just noticeable learning in the sub-networks of reconstruction and segmentation to di-rectly localize the defects without any complex additional post-processing.

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