Web: http://arxiv.org/abs/2209.09858

Sept. 21, 2022, 1:13 a.m. | Andrija Djurisic, Nebojsa Bozanic, Arjun Ashok, Rosanne Liu

cs.CV updates on arXiv.org arxiv.org

The separation between training and deployment of machine learning models
implies that not all scenarios encountered in deployment can be anticipated
during training, and therefore relying solely on advancements in training has
its limits. Out-of-distribution (OOD) detection is an important area that
stress-tests a model's ability to handle unseen situations: Do models know when
they don't know? Existing OOD detection methods either incur extra training
steps, additional data or make nontrivial modifications to the trained network.
In contrast, in this …

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