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@inproceedings{2392228, author = {Hashemi, Vahid and Křetínský, Jan and Rieder, Sabine and Schmidt, Jessica}, address = {Lübeck}, booktitle = {FORMAL METHODS, FM 2023}, doi = {http://dx.doi.org/10.1007/978-3-031-27481-7_36}, editor = {978-3-031-27481-7}, keywords = {Runtime monitoring; Neural networks; Out-of-distribution detection; Object detection}, howpublished = {tištěná verze "print"}, language = {eng}, location = {Lübeck}, isbn = {978-3-031-27480-0}, pages = {622-634}, publisher = {SPRINGER INTERNATIONAL PUBLISHING AG}, title = {Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks}, year = {2023} }
TY - JOUR ID - 2392228 AU - Hashemi, Vahid - Křetínský, Jan - Rieder, Sabine - Schmidt, Jessica PY - 2023 TI - Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks PB - SPRINGER INTERNATIONAL PUBLISHING AG CY - Lübeck SN - 9783031274800 KW - Runtime monitoring KW - Neural networks KW - Out-of-distribution detection KW - Object detection N2 - Runtime monitoring provides a more realistic and applicable alternative to verification in the setting of real neural networks used in industry. It is particularly useful for detecting out-of-distribution (OOD) inputs, for which the network was not trained and can yield erroneous results. We extend a runtime-monitoring approach previously proposed for classification networks to perception systems capable of identification and localization of multiple objects. Furthermore, we analyze its adequacy experimentally on different kinds of OOD settings, documenting the overall efficacy of our approach. ER -
HASHEMI, Vahid, Jan KŘETÍNSKÝ, Sabine RIEDER and Jessica SCHMIDT. Runtime Monitoring for Out-of-Distribution Detection in Object Detection Neural Networks. In 978-3-031-27481-7. \textit{FORMAL METHODS, FM 2023}. Lübeck: SPRINGER INTERNATIONAL PUBLISHING AG, 2023, p.~622-634. ISBN~978-3-031-27480-0. Available from: https://dx.doi.org/10.1007/978-3-031-27481-7\_{}36.
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