  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.
