标题： 分布外检测方法回答了错误的问题 显示英文标题

标题： Out-of-Distribution Detection Methods Answer the Wrong Questions

摘要： 为了检测分布偏移并提高模型安全性，许多分布外（OOD）检测方法依赖于在分布内数据上训练的监督模型的预测不确定性或特征。 在本文中，我们对这一流行的OOD检测方法进行了深入的重新审视，并认为这些方法在OOD检测中本质上是回答了错误的问题。 这种不匹配没有简单的解决办法，因为仅在分布内类别上训练的分类器无法被期望识别分布外点；例如，一只猫狗分类器可能会自信地将一架飞机误分类，如果它包含区分猫和狗的特征，尽管它们通常看起来完全不同。 我们发现基于不确定性的方法错误地将高不确定性与分布外混为一谈，而基于特征的方法则错误地将特征空间中的远距离与分布外混为一谈。 我们展示了这些病理现象如何作为OOD检测中的不可减少的错误表现出来，并确定了这些方法无效的常见场景。 此外，旨在提高OOD检测的干预措施，如特征-逻辑混合方法、模型和数据规模的扩展、认识论不确定性表示以及异常暴露，也未能解决目标的根本性不匹配。 我们还考虑了用于OOD检测的无监督密度估计和生成模型，我们表明它们也有其自身的根本限制。 显示英文摘要

摘要： To detect distribution shifts and improve model safety, many out-of-distribution (OOD) detection methods rely on the predictive uncertainty or features of supervised models trained on in-distribution data. In this paper, we critically re-examine this popular family of OOD detection procedures, and we argue that these methods are fundamentally answering the wrong questions for OOD detection. There is no simple fix to this misalignment, since a classifier trained only on in-distribution classes cannot be expected to identify OOD points; for instance, a cat-dog classifier may confidently misclassify an airplane if it contains features that distinguish cats from dogs, despite generally appearing nothing alike. We find that uncertainty-based methods incorrectly conflate high uncertainty with being OOD, while feature-based methods incorrectly conflate far feature-space distance with being OOD. We show how these pathologies manifest as irreducible errors in OOD detection and identify common settings where these methods are ineffective. Additionally, interventions to improve OOD detection such as feature-logit hybrid methods, scaling of model and data size, epistemic uncertainty representation, and outlier exposure also fail to address this fundamental misalignment in objectives. We additionally consider unsupervised density estimation and generative models for OOD detection, which we show have their own fundamental limitations.