Title:	Neural Criticality: Validation of Convolutional Neural Networks
Authors:	Diviš, Václav Hrúz, Marek
Citation:	DIVIŠ, V. HRÚZ, M. Neural Criticality: Validation of Convolutional Neural Networks. In CEUR Workshop Proceedings. Neuveden: CEUR-WS, 2021. s. nestránkováno. ISBN: neuvedeno , ISSN: 1613-0073
Issue Date:	2021
Publisher:	CEUR-WS
Document type:	konferenční příspěvek ConferenceObject
URI:	2-s2.0-85101278318 http://hdl.handle.net/11025/47141
ISBN:	neuvedeno
ISSN:	1613-0073
Keywords in different language:	Criticality;Validation;Convolutional Neural Networks
Abstract in different language:	The black-box behavior of Convolutional Neural Networks is one of the biggest obstacles to the development of a standardized validation process. Methods for analyzing and validating neural networks currently rely on approaches and metrics provided by the scientific community without considering functional safety requirements. However, automotive norms, such as ISO26262 and ISO/PAS21448, do require a comprehensive knowledge of the system and of the working environment in which the network will be deployed. In order to gain such a knowledge and mitigate the natural uncertainty of probabilistic models, we focused on investigating the influence of filter weights on the classification confidence in Single Point Of Failure fashion. We laid the theoretical foundation of a method called the Neurons’ Criticality Analysis. This method, as described in this article, helps evaluate the criticality of the tested network and choose related plausibility mechanism. Copyright © 2021, for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Rights:	Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © authors
Appears in Collections:	Konferenční příspěvky / Conference Papers (KKY) OBD

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