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arXiv · 2212.12641

Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty

Genki Osada·Takahashi Tsubasa·Budrul Ahsan·Takashi Nishide
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Abstract

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

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Genki Osada, Takahashi Tsubasa, Budrul Ahsan, Takashi Nishide. 2022-12-24. Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty. https://arxiv.org/abs/2212.12641

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