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pro vyhledávání: '"surprise adequacy"'
Akademický článek
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Surprise Adequacy (SA) is one of the emerging and most promising adequacy criteria for Deep Learning (DL) testing. As an adequacy criterion, it has been used to assess the strength of DL test suites. In addition, it has also been used to find inputs
Externí odkaz:
http://arxiv.org/abs/2103.05939
Reducing DNN Labelling Cost using Surprise Adequacy: An Industrial Case Study for Autonomous Driving
Deep Neural Networks (DNNs) are rapidly being adopted by the automotive industry, due to their impressive performance in tasks that are essential for autonomous driving. Object segmentation is one such task: its aim is to precisely locate boundaries
Externí odkaz:
http://arxiv.org/abs/2006.00894
Deep Learning (DL) systems are rapidly being adopted in safety and security critical domains, urgently calling for ways to test their correctness and robustness. Testing of DL systems has traditionally relied on manual collection and labelling of dat
Externí odkaz:
http://arxiv.org/abs/1808.08444
Akademický článek
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Akademický článek
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Publikováno v:
Applied Sciences, Vol 11, Iss 15, p 6826 (2021)
Facing the increasing quantity of AI models applications, especially in life- and property-related fields, it is crucial for designers to construct safety- and security-critical systems. As a major factor affecting the safety of AI models, corner cas
Externí odkaz:
https://doaj.org/article/8881d140ae6443d19136765c71a80940
Publikováno v:
ACM Transactions on Software Engineering and Methodology. 32:1-29
The rapid adoption of Deep Learning (DL) systems in safety critical domains such as medical imaging and autonomous driving urgently calls for ways to test their correctness and robustness. Borrowing from the concept of test adequacy in traditional so
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::de7b03001d7000323483245fd73487ed
https://doi.org/10.1145/3546947
https://doi.org/10.1145/3546947
Publikováno v:
Applied Sciences, Vol 11, Iss 6826, p 6826 (2021)
Applied Sciences
Volume 11
Issue 15
Applied Sciences
Volume 11
Issue 15
Facing the increasing quantity of AI models applications, especially in life- and property-related fields, it is crucial for designers to construct safety- and security-critical systems. As a major factor affecting the safety of AI models, corner cas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8daaaa35fdf0418da18fb90f14da9078
https://www.mdpi.com/2076-3417/11/15/6826
https://www.mdpi.com/2076-3417/11/15/6826
Publikováno v:
Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0af87732b4b75426d7b28fbc64e97de
https://doi.org/10.1145/3522664.3528617
https://doi.org/10.1145/3522664.3528617