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The Value of Out-of-Distribution Data. (arXiv:2208.10967v2 [cs.LG] UPDATED)
Oct. 7, 2022, 1:14 a.m. | Ashwin De Silva, Rahul Ramesh, Carey E. Priebe, Pratik Chaudhari, Joshua T. Vogelstein
stat.ML updates on arXiv.org arxiv.org
More data is expected to help us generalize to a task. But real datasets can
contain out-of-distribution (OOD) data; this can come in the form of
heterogeneity such as intra-class variability but also in the form of temporal
shifts or concept drifts. We demonstrate a counter-intuitive phenomenon for
such problems: generalization error of the task can be a non-monotonic function
of the number of OOD samples; a small number of OOD samples can improve
generalization but if the number of …
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