  We critically appraise the recent interest in out-of-distribution (OOD)
detection and question the practical relevance of existing benchmarks. While
the currently prevalent trend is to consider different datasets as OOD, we
argue that out-distributions of practical interest are ones where the
distinction is semantic in nature for a specified context, and that evaluative
tasks should reflect this more closely. Assuming a context of object
recognition, we recommend a set of benchmarks, motivated by practical
applications. We make progress on these benchmarks by exploring a multi-task
learning based approach, showing that auxiliary objectives for improved
semantic awareness result in improved semantic anomaly detection, with
accompanying generalization benefits.
