Detecting semantic anomalies

Faruk Ahmed; Aaron Courville

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arXiv · 1908.04388

Detecting semantic anomalies

Faruk Ahmed·Aaron Courville

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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.

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Faruk Ahmed, Aaron Courville. 2019-08-13. Detecting semantic anomalies. https://arxiv.org/abs/1908.04388

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