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- W2968923568 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." @default.
- W2968923568 created "2019-08-22" @default.
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- W2968923568 date "2019-08-13" @default.
- W2968923568 modified "2023-10-01" @default.
- W2968923568 title "Detecting semantic anomalies" @default.
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