用固定参考值替换激活排序，提升分布外检测一致性。
Ranked Activation Shift for Post-Hoc Out-of-Distribution Detection
- 用固定分布参考替代排序激活，无需调参。
- 跨数据集与模型表现稳定，不依赖激活函数形式。
- 适合需要可靠检测但无调参资源的场景。
当前先进的后验分布外检测方法依赖中间层激活编辑，但在不同数据集和模型上表现不一。我们发现这种不稳定性源于激活分布差异，并识别出当倒数第二层激活未修正时，基于缩放的方法存在失效模式。为此，我们提出RAS，一种无需超参数的后验方法：将排序后的激活幅度替换为固定的分布内参考轮廓。该简单即插即用的方法在不同数据集和架构上均表现强劲且一致，无需对倒数第二层激活函数做假设，也无需调参，同时保持分布内分类准确率。进一步分析表明，抑制和增强激活变化各自独立地促进更好的分布外判别能力。
原文摘要 · Abstract (English)
State-of-the-art post-hoc out-of-distribution detection methods rely on intermediate layer activation editing. However, they exhibit inconsistent performance across datasets and models. We show that this instability is driven by differences in the activation distributions, and identify a failure mode of scaling-based methods that arises when penultimate layer activations are not rectified. Motivated by this analysis, we propose RAS, a hyperparameter-free post-hoc method that replaces sorted activation magnitudes with a fixed in-distribution reference profile. Our simple plug-and-play method shows strong and consistent performance across datasets and architectures without assumptions on the penultimate layer activation function, and without requiring any hyperparameter tuning, while empirically preserving in-distribution classification accuracy. We further analyze what drives the improvement, showing that both inhibiting and exciting activation shifts independently contribute to better out-of-distribution discrimination.
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