# Detecting semantic anomalies

## TL;DR

This paper critiques current out-of-distribution detection benchmarks, emphasizing the importance of semantic distinctions in practical scenarios, and proposes a multi-task learning approach to improve semantic anomaly detection.

## Contribution

It introduces contextually relevant benchmarks for semantic OOD detection and demonstrates that auxiliary objectives enhance semantic awareness and detection performance.

## Key findings

- Multi-task learning improves semantic anomaly detection.
- Auxiliary objectives enhance semantic awareness.
- Proposed benchmarks reflect practical applications.

## Abstract

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.