Determining whether inputs are out-of-distribution (OOD) is an essential building block for safely deploying machine learning models in the open world. However, previous methods relying on the softmax confidence score suffer from overconfident posterior distributions for OOD data. We propose a unified framework for OOD detection that uses an energy score. We show that energy scores better distinguish in- and out-of-distribution samples than the traditional approach using the softmax scores. Unlike softmax confidence scores, energy scores are theoretically aligned with the probability density of the inputs and are less susceptible to the overconfidence issue. Within this framework, energy can be flexibly used as a scoring function for any pre-trained neural classifier as well as a trainable cost function to shape the energy surface explicitly for OOD detection. On a CIFAR-10 pre-trained WideResNet, using the energy score reduces the average FPR (at TPR 95%) by 18.03% compared to the softmax confidence score. With energy-based training, our method outperforms the state-of-the-art on common benchmarks.

在开放世界中安全部署机器学习模型的重要基础是确定输入是否为ODD, 然而，传统基于softmax置信度得分的方法在ODD数据的后验分布上存在过度自信的问题，本文提出了一个使用能量分数的ODD检测的统一框架，理论上更能够识别“内部”和“外部”数据。这个框架内可以灵活地将能量用作得分函数和OOR检测的可训练成本函数，与CIFAR-10预训练的WideResNet相比，使用能量分数将平均FPR （在TPR 95％处）降低了18.03％，并且使用基于能量的训练，我们的方法在常见基准上表现优异。

基于能量的区分度检测