@inproceedings{6da8f3728c4445cea63df99ac996c914,
title = "Gaussian-Based Runtime Detection of Out-of-distribution Inputs for Neural Networks",
abstract = "In this short paper, we introduce a simple approach for runtime monitoring of deep neural networks and show how to use it for out-of-distribution detection. The approach is based on inferring Gaussian models of some of the neurons and layers. Despite its simplicity, it performs better than recently introduced approaches based on interval abstractions which are traditionally used in verification.",
author = "Vahid Hashemi and Jan K{\v r}et{\'i}nsk{\'y} and Stefanie Mohr and Emmanouil Seferis",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 21st International Conference on Runtime Verification, RV 2021 ; Conference date: 11-10-2021 Through 14-10-2021",
year = "2021",
doi = "10.1007/978-3-030-88494-9\_14",
language = "English",
isbn = "9783030884932",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "254--264",
editor = "Lu Feng and Dana Fisman",
booktitle = "Runtime Verification - 21st International Conference, RV 2021, Proceedings",
}