论文标题
ML研究人员对OOD检测的虚假性
Falsehoods that ML researchers believe about OOD detection
论文作者
论文摘要
一种直观的检测分布外（OOD）数据的方式是通过拟合概率生成模型的密度函数：低密度的点可以归类为OOD。但是，在深度学习环境中，发现这种方法失败了。在本文中，我们列出了机器学习研究人员对基于密度的OOD检测的一些虚假性。许多最近的作品提出了基于似然比的方法来“解决”问题。我们提出了一个框架，即OOD代理框架，以统一这些方法，并认为似然比是OOD检测的原则方法，而不是单纯的“ fix”。最后，我们讨论领域歧视与语义之间的关系。
An intuitive way to detect out-of-distribution (OOD) data is via the density function of a fitted probabilistic generative model: points with low density may be classed as OOD. But this approach has been found to fail, in deep learning settings. In this paper, we list some falsehoods that machine learning researchers believe about density-based OOD detection. Many recent works have proposed likelihood-ratio-based methods to `fix' the problem. We propose a framework, the OOD proxy framework, to unify these methods, and we argue that likelihood ratio is a principled method for OOD detection and not a mere `fix'. Finally, we discuss the relationship between domain discrimination and semantics.
