文章基本信息

标题：Fault Tolerant Deep Neural Networks for Detection of Unrecognizable Situations
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作者：Kaoutar Rhazali ; Benjamin Lussier ; Walter Schön 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2018
卷号：51
期号：24
页码：31-37
DOI：10.1016/j.ifacol.2018.09.525
语种：English
出版社：Elsevier
摘要：AbstractDeep Neural Networks are achieving great success in various fields. However, their use remains limited to non critical applications because their behavior is unpredictable and unsafe. In this paper we propose some fault tolerant approaches based on diversifying learning in order to improve DNNs dependability and particularly safety. Our main goal is to increase trust in the outcome of deep learning mechanisms by recognizing the unlearned inputs and preventing misclassification.
关键词：KeywordsSafetyFault ToleranceArtificial IntelligenceNeural NetworksAutonomous Vehicles