As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence. Among many others, existing methods use the following two scores to do so without training on any apriori OOD examples: a learned temperature and an energy score. In this paper we introduce Ablated Learned Temperature Energy (or "AbeT" for short), a method which combines these prior methods in novel ways with effective modifications. Due to these contributions, AbeT lowers the False Positive Rate at $95\%$ True Positive Rate (FPR@95) by $35.39\%$ in classification (averaged across all ID and OOD datasets measured) compared to state of the art without training networks in multiple stages or requiring hyperparameters or test-time backward passes. We additionally provide empirical insights as to how our model learns to distinguish between In-Distribution (ID) and OOD samples while only being explicitly trained on ID samples via exposure to misclassified ID examples at training time. Lastly, we show the efficacy of our method in identifying predicted bounding boxes and pixels corresponding to OOD objects in object detection and semantic segmentation, respectively - with an AUROC increase of $5.15\%$ in object detection and both a decrease in FPR@95 of $41.48\%$ and an increase in AUPRC of $34.20\%$ on average in semantic segmentation compared to previous state of the art.

翻译：随着深度神经网络在高风险领域的广泛应用，在推理输入为分布外样本时进行识别至关重要，从而使用户能够在模型高置信度输出的情况下，及时获知可能出现的性能下降与校准失效。在众多现有方法中，有两类无需预先生成分布外样本即可实现检测的评分机制：学习温度评分与能量评分。本文提出一种名为"分布外检测与消融学习温度能量"（简称AbeT）的新方法，该方法创新性地融合上述两类方法并引入有效改进。凭借这些改进，AbeT在分类任务中将假阳性率（在真阳性率为95%时）平均降低了35.39%（覆盖所有测得的ID和OOD数据集），且无需多阶段训练网络、无需超参数或测试时反向传播。我们进一步提供实证分析，阐明模型如何在仅通过训练阶段暴露于分类错误的ID样本这一条件下，学会区分分布内样本与分布外样本。最后，我们展示了该方法在目标检测和语义分割任务中的有效性——分别识别对应分布外物体的预测边界框与像素。与现有最优方法相比，目标检测的AUROC提升5.15%，语义分割的FPR@95平均降低41.48%，AUPRC平均提升34.20%。