@inproceedings{6d1f96e59237481f98d73409bcaa6559,
title = "Considering Reliability of Deep Learning Function to Boost Data Suitability and Anomaly Detection",
abstract = "The increased demand of Deep Neural Networks (DNNs) in safety-critical systems, such as autonomous vehicles, leads to increasing importance of training data suitability. Firstly, we focus on how to extract the relevant data content for ensuring DNN reliability. Then, we identify error categories and propose mitigation measures with emphasis on data suitability. Despite all efforts to boost data suitability, not all possible variations of a real application can be identified. Hence, we analyse the case of unknown out-of-distribution data. In this case, we suggest to complement data suitability with online anomaly detection using FACER that supervises the behaviour of the DNN.",
keywords = "anomaly detection, data analysis, data suitability",
author = "Lydia Gauerhof and Yuki Hagiwara and Christoph Schorn and Mario Trapp",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 31st IEEE International Symposium on Software Reliability Engineering Workshops, ISSREW 2020 ; Conference date: 12-10-2020 Through 15-10-2020",
year = "2020",
month = oct,
doi = "10.1109/ISSREW51248.2020.00081",
language = "English",
isbn = "9781728177359",
series = "Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "249--254",
editor = "Marco Vieira and Henrique Madeira and Nuno Antunes and Zheng Zheng",
booktitle = "Proceedings - 2020 IEEE 31st International Symposium on Software Reliability Engineering Workshops, ISSREW 2020",
}