Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Lydia Gauerhof"'
Publikováno v:
2022 IEEE 27th Pacific Rim International Symposium on Dependable Computing (PRDC).
Autor:
Christian Heinzemann, Stephanie Abrecht, Christoph Gladisch, Matthias Woehrle, Konrad Groh, Lydia Gauerhof
Publikováno v:
ACM Transactions on Cyber-Physical Systems. 5:1-28
Due to the impressive performance of deep neural networks (DNNs) for visual perception, there is an increased demand for their use in automated systems. However, to use deep neural networks in practice, novel approaches are needed, e.g., for testing.
Publikováno v:
2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW).
Publikováno v:
ISSRE Workshops
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 r
Autor:
Christoph Schorn, Lydia Gauerhof
Publikováno v:
ITSC
The detection of anomalies during the operation of deep neural networks (DNNs) is of essential importance in safety-critical applications, such as autonomous vehicles. In the field, classifiers may face rare environmental conditions, unknown objects,
Autor:
Gu Nianlong, Lydia Gauerhof
Publikováno v:
WACV
In this paper, we introduce the ‘Reverse Variational Autoencoder" (Reverse-VAE) which is a generative network. On the one hand, visual attributes can be manipulated and combined while generating images. On the other hand, anomalies, meaning deviati
Autor:
Klaus Janschek, Kai Ding, Emil Valiev, Lydia Gauerhof, Andrey Morozov, Christoph Schorn, Michael Beyer
Today, Deep Learning (DL) enhances almost every industrial sector, including safety-critical areas. The next generation of safety standards will define appropriate verification techniques for DL-based applications and propose adequate fault tolerance
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3eb69491c15fb15332046174761fcbf0
Autor:
Gesina Schwalbe, Lydia Gauerhof, Shervin Raafatnia, Bernhard Knie, Vittorio Rocco, Timo Dobberphul, Timo Sämann
Publikováno v:
Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops ISBN: 9783030555825
SAFECOMP Workshops
SAFECOMP Workshops
Deep neural networks (DNNs) are widely considered as a key technology for perception in high and full driving automation. However, their safety assessment remains challenging, as they exhibit specific insufficiencies: black-box nature, simple perform
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c10cfc6144d967f9bc94df466ee115f
https://doi.org/10.1007/978-3-030-55583-2_29
https://doi.org/10.1007/978-3-030-55583-2_29
Publikováno v:
Intelligent Internet of Things ISBN: 9783030303662
Cyber-physical systems (CPS) have broad applications in the automotive, avionics, robotics, healthcare, and power grid, where the cyber components involving information processing and networking closely interact with the physical processes. Conventio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::49faf86155a59da177bca1699c1faae8
https://doi.org/10.1007/978-3-030-30367-9_7
https://doi.org/10.1007/978-3-030-30367-9_7
Autor:
Ibrahim Habli, Richard Hawkins, Lydia Gauerhof, Colin Paterson, Yuki Hagiwara, Chiara Picardi
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030545482
SAFECOMP
SAFECOMP
Machine Learnt Models (MLMs) are now commonly used in self-driving cars, particularly for tasks such as object detection and classification within the perception pipeline. The failure of such models to perform as intended could lead to hazardous even
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1462731c6ce60d35fe4fc0e6abb75a3e
https://doi.org/10.1007/978-3-030-54549-9_13
https://doi.org/10.1007/978-3-030-54549-9_13