PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation

@article{Reiss2021PANDAAP,
  title={PANDA: Adapting Pretrained Features for Anomaly Detection and Segmentation},
  author={Tal Reiss and Niv Cohen and Liron Bergman and Yedid Hoshen},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={2805-2813}
}
  • Tal Reiss, Niv Cohen, Yedid Hoshen
  • Published 12 October 2020
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Anomaly detection methods require high-quality features. In recent years, the anomaly detection community has attempted to obtain better features using advances in deep self-supervised feature learning. Surprisingly, a very promising direction, using pre-trained deep features, has been mostly overlooked. In this paper, we first empirically establish the perhaps expected, but unreported result, that combining pre-trained features with simple anomaly detection and segmentation methods… 
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