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    • SeaS

      Public
      [arXiv:2410.14987] SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning. Paper is available at https://arxiv.org/abs/2410.14987
      MIT License
      01710Updated Jun 8, 2024Jun 8, 2024
    • MuSc

      Public
      [ICLR2024] MuSc : Zero-Shot Industrial Anomaly Classification and Segmentation with Mutual Scoring of the Unlabeled Images (The original repo is available at https://github.com/xrli-U/MuSc)
      Python
      MIT License
      0900Updated Jun 7, 2024Jun 7, 2024
    • [arXiv:2410.14379] AnomalyNCD: Towards Novel Anomaly Class Discovery in Industrial Scenarios. Paper is available at https://arxiv.org/abs/2410.14379
      MIT License
      02120Updated Jun 7, 2024Jun 7, 2024