参考重采样下OOD分数的判决不稳定性
Verdict Instability of OOD Scores under Reference Resampling
- Hongik University（弘益大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究提出判决不稳定性概念，通过重采样参考集衡量OOD检测器分数的判决变动，给出闭式解，发现远OOD查询对应可复现判决，符号错误的弃权效果差。
AI中文摘要：
事后分布外（OOD）检测器基于有限参考集拟合，因此其输出的每个分数都是估计值。若选择不同的参考集，部分判决结果会发生变化。我们通过重采样参考集并记录分数的自助法标准差来衡量这种变化，将其称为判决不稳定性。该不稳定性存在无拟合参数的闭式解，其定义为：判决的不稳定性是分配类别沿查询方向的类内离散度除以该类参考数量的平方根。该参考数量是判决不稳定性与分数分布几何结构的区分因素，且仅在类别不平衡时可识别。不稳定性随局部离散度增大而增长。远OOD查询位于各向异性嵌入的低方差方向，因此我们测试的所有基于距离的分数均将最高值分配给最具可复现性的判决。仅局部离散度的估计器具备从业者期望的符号，我们给出一条规则，可通过单个无标签相关关系预测任意分数的该符号，且在我们测试的所有数据集上，由符号错误的分数驱动的弃权（不执行）比随机弃权效果更差。
英文摘要：
Post-hoc out-of-distribution detectors are fitted on a finite reference set, so every score they produce is an estimate. If we had chosen a different set, some verdicts would have moved. We measure that movement by resampling the reference set and recording the bootstrap standard deviation of the score, which we call verdict instability. It admits a closed form with no fitted parameters. The instability of a verdict is the within-class dispersion of the assigned class along the query's direction, divided by the square root of that class's reference count. That count is what separates verdict instability from the geometry of the score distribution, and it is identifiable only under class imbalance. Instability grows with the local dispersion. Far-OOD queries lie along the low-variance directions of an anisotropic embedding, so every distance-based score we test assigns its highest values to the verdicts that are most reproducible. Only estimators of local dispersion carry the sign a practitioner expects. We give a rule that predicts this sign for any score from a single label-free correlation, and abstention driven by a wrong-signed score turns out worse than abstention at random on every dataset we test.
