Detecting semantic anomalies

We critically appraise the recent interest in out-of-distribution (OOD) detection, questioning the practical relevance of existing benchmarks. While the currently prevalent trend is to consider different datasets as OOD, we posit that out-distributions of practical interest are ones where the distinction is semantic in nature, and evaluative tasks should reflect this more closely. Assuming a context of computer vision object recognition problems, we then recommend a set of benchmarks which we motivate by referencing practical applications of anomaly detection. Finally, we explore a multi-task learning based approach which suggests that auxiliary objectives for improved semantic awareness can result in improved semantic anomaly detection, with accompanying generalization benefits.