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Unsupervised Out-of-Distribution Detection with Batch Normalization
[article]
2019
arXiv
pre-print
Likelihood from a generative model is a natural statistic for detecting out-of-distribution (OoD) samples. However, generative models have been shown to assign higher likelihood to OoD samples compared to ones from the training distribution, preventing simple threshold-based detection rules. We demonstrate that OoD detection fails even when using more sophisticated statistics based on the likelihoods of individual samples. To address these issues, we propose a new method that leverages batch
arXiv:1910.09115v1
fatcat:dggjj4ldwng2xfwqqqkkqppene