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少样本异常检测阈值认证极限研究

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2026-10-02 arXiv 发表机器学习理论研究,使用冻结 DINOv2 PCA 残差排序器,在 15 个 MVTec 与 12 个 VisA 类别上验证少样本异常阈值校准。目标仅留一图(LOIO)方案在 α=0.20 水平下经验 FAR 达 0.341,为名义水平的 1.7 倍,揭示分布无关阈值认证的可行性边界。

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Oct 2, 2026
  1. arXiv · Machine Learning Theory
    少样本异常检测中,分布无关的阈值认证需要多少类别样本?——DINOv2 PCA 残差排序器的可行性边界与 CRESS 协议

    研究用冻结 DINOv2 PCA 残差排序器在 15 个 MVTec 和 12 个 VisA 类别上验证少样本异常阈值:目标仅留一图(LOIO)校准在 α=0.20 时经验 FAR 达 0.341,为名义水平的 1.7 倍。

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