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Automated Testing of Graphics Units by Deep-Learning Detect..:

, In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining,

Faivishevsky, Lev ; Szeskin, Adi ; Muppalla, Ashwin K.... - p. 2811-2821 , 2021

Link: https://dl.acm.org/doi/10.1145/3447548.3467116

RT 
T1 <cTitle>Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining</cTitle>: T1 <Title>Automated Testing of Graphics Units by Deep-Learning Detection of Visual Anomalies</Title>

UL https://suche.suub.uni-bremen.de/peid=acm-3467116&Exemplar=1&LAN=DE
				
A1 Faivishevsky, Lev
A1 Szeskin, Adi
A1 Muppalla, Ashwin K.
A1 Shwartz-Ziv, Ravid
A1 Ben Ari, Itamar
A1 Laperdon, Ronen
A1 Melloul, Benjamin
A1 Hollander, Tahi
A1 Hope, Tom
A1 Armon, Amitai
PB ACM
YR 2021
K1 anomaly detection
K1 computer vision
K1 deep learning
K1 graphics processors validation
K1 multiple instance learning
K1 Computing methodologies
K1 Artificial intelligence
K1 Computer vision
K1 Computer vision tasks
K1 Visual inspection
K1 Software and its engineering
K1 Software creation and management
K1 Software verification and validation
K1 Empirical software validation
K1 Hardware
K1 Hardware validation
K1 Post-manufacture validation and debug
K1 Bug detection, localization and diagnosis
K1 Scene anomaly detection
SP 2811
OP 2821
LK http://dx.doi.org/https://dl.acm.org/doi/10.1145/3447548.3467116
DO https://dl.acm.org/doi/10.1145/3447548.3467116
SF ELIB - SuUB Bremen

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