Web: http://arxiv.org/abs/2206.09522

June 23, 2022, 1:12 a.m. | Akshayaa Magesh, Venugopal V. Veeravalli, Anirban Roy, Susmit Jha

stat.ML updates on arXiv.org arxiv.org

We study the problem of Out-of-Distribution (OOD) detection, that is,
detecting whether a learning algorithm's output can be trusted at inference
time. While a number of tests for OOD detection have been proposed in prior
work, a formal framework for studying this problem is lacking. We propose a
definition for the notion of OOD that includes both the input distribution and
the learning algorithm, which provides insights for the construction of
powerful tests for OOD detection. We propose a multiple …

arxiv detection distribution framework ml testing

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