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

• URL: http://arxiv.org/abs/2212.12641v1
• Date: Sat, 24 Dec 2022 03:10:28 GMT
• Title: Out-of-Distribution Detection with Reconstruction Error and Typicality-based Penalty
• Authors: Genki Osada, Takahashi Tsubasa, Budrul Ahsan, Takashi Nishide
• Abstract summary: We propose a new reconstruction error-based approach that employs normalizing flow (NF) 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.
• Score: 3.7277730514654555
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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