AOE：通过重新校准异常标签实现穷尽式分布外检测
AOE: Exhaustive Out-of-Distribution Detection via Recalibrating Outlier Labels
AI总结：
提出自适应置信度异常暴露（AOE）方法，通过温度缩放重新校准异常标签，利用自适应软目标保留分布外样本与分布内类别的语义关系，从而扩大分离边界并提升分布外检测性能。
摘要：
分布外（OOD）检测对于在开放世界和安全关键场景中部署机器学习模型至关重要，在这些场景中，测试输入可能偏离训练分布，对未知样本的过度自信预测可能导致不可靠的决策。异常暴露（OE）通过训练期间引入辅助异常样本来扩大分布内（ID）和OOD样本之间的间隔，已成为一种有前景的OOD检测范式。现有的基于OE的方法通常通过使用统一标签来最大化OOD样本在ID类别上的熵，从而扩大这一间隔。然而，我们从理论上证明，统一标签不可避免地忽略了OOD样本与ID类别之间的关系，称为过度软化效应，导致次优的间隔边界。我们的理论分析进一步揭示，显式利用这种关系反而可以提高OOD检测性能。受此启发，我们提出了自适应置信度异常暴露（AOE），一种简单而有效的方法，利用温度缩放重新校准异常标签。具体来说，AOE从温度缩放的模型预测中为OOD样本生成自适应软目标，其中可学习的温度平滑预测分布，而不会完全消除类别关系信息。通过使用这些自适应软目标监督OOD样本，AOE保留了OOD样本与ID类别之间的语义接近性，同时鼓励软目标接近高熵分布，从而抑制过度自信的OOD预测并扩大分离边界。在多种基准上的大量实验证明了AOE的有效性。
英文摘要：
Out-of-distribution (OOD) detection is essential for deploying machine learning models in open-world and safety-critical scenarios, where test inputs may deviate from the training distribution and overconfident predictions on unknown samples can lead to unreliable decisions. Outlier Exposure (OE) has emerged as a promising OOD detection paradigm by introducing auxiliary outliers during training to enlarge the margin between in-distribution (ID) and OOD samples. Existing OE-based methods typically enlarge this margin by employing uniform labels to maximize the entropy of OOD samples over ID categories. However, we theoretically show that uniform labels inevitably disregard the relations between OOD samples and ID categories, termed the over-softening effect, leading to a suboptimal margin bound. Our theoretical analysis further reveals that explicitly exploiting such relations can instead yield improved OOD detection performance. Motivated by this insight, we propose \underline{A}daptive Confidence \underline{OE} (AOE), a simple yet effective method that leverages temperature scaling to recalibrate outlier labels. Specifically, AOE generates adaptive soft targets from temperature-scaled model predictions for OOD samples, where the learnable temperature smooths the prediction distribution without fully erasing class-wise relational information. By supervising OOD samples with these adaptive soft targets, AOE preserves the semantic proximity between OOD samples and ID categories while encouraging the softened targets to approach a high-entropy distribution, thereby suppressing overconfident OOD predictions and enlarging the separation margin. Extensive experiments across diverse benchmarks demonstrate the effectiveness of AOE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
