  We introduce a principled approach to detecting out-of-distribution (OOD)
data by exploiting a connection to data curation. In data curation, we exclude
ambiguous or difficult-to-classify input points from the dataset, and these
excluded points are by definition OOD. We can therefore obtain the likelihood
for OOD points by using a principled generative model of data-curation
initially developed to explain the cold-posterior effect in Bayesian neural
networks (Aitchison 2020). This model gives higher OOD probabilities when
predictive uncertainty is higher and can be trained using maximum-likelihood
jointly over the in-distribution and OOD points. This approach gives superior
performance to past methods that did not provide a probability for OOD points,
and therefore could not be trained using maximum-likelihood.
