  Out-of-distribution detection is an important component of reliable ML
systems. Prior literature has proposed various methods (e.g., MSP (Hendrycks &
Gimpel, 2017), ODIN (Liang et al., 2018), Mahalanobis (Lee et al., 2018)),
claiming they are state-of-the-art by showing they outperform previous methods
on a selected set of in-distribution (ID) and out-of-distribution (OOD)
datasets. In this work, we show that none of these methods are inherently
better at OOD detection than others on a standardized set of 16 (ID, OOD)
pairs. We give possible explanations for these inconsistencies with simple toy
datasets where whether one method outperforms another depends on the structure
of the ID and OOD datasets in question. Finally, we show that a method
outperforming another on a certain (ID, OOD) pair may not do so in a low-data
regime. In the low-data regime, we propose a distance-based method, Pairwise
OOD detection (POD), which is based on Siamese networks and improves over
Mahalanobis by sidestepping the expensive covariance estimation step. Our
results suggest that the OOD detection problem may be too broad, and we should
consider more specific structures for leverage.
