It is an important problem in trustworthy machine learning to recognize out-of-distribution (OOD) inputs which are inputs unrelated to the in-distribution task. Many out-of-distribution detection methods have been suggested in recent years. The goal of this paper is to recognize common objectives as well as to identify the implicit scoring functions of different OOD detection methods. We focus on the sub-class of methods that use surrogate OOD data during training in order to learn an OOD detection score that generalizes to new unseen out-distributions at test time. We show that binary discrimination between in- and (different) out-distributions is equivalent to several distinct formulations of the OOD detection problem. When trained in a shared fashion with a standard classifier, this binary discriminator reaches an OOD detection performance similar to that of Outlier Exposure. Moreover, we show that the confidence loss which is used by Outlier Exposure has an implicit scoring function which differs in a non-trivial fashion from the theoretically optimal scoring function in the case where training and test out-distribution are the same, which again is similar to the one used when training an Energy-Based OOD detector or when adding a background class. In practice, when trained in exactly the same way, all these methods perform similarly.

本文旨在识别常见的目标，以及识别不同OOD检测方法的隐含评分函数。我们展示了许多方法在共享学习方式下表现相似，二进制区分器达到与异常暴露相似的OOD检测性能，还展示了置信度损失具有在理论上最优得分函数不同但与训练和测试out-distribution相同时的函数相似的隐含评分函数，从实践中发现，这些方法训练方式一致时都表现相似。

基于OOD训练数据的许多方法估计相同核心量的组合来分解OD检测