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Poster (Scientific congresses, symposiums and conference proceedings)
Revisiting the Training of Very Deep Neural Networks without Skip Connections
Oyedotun, Oyebade; Shabayek, Abd El Rahman; Aouada, Djamila et al.
2021 • IEEE 2020 International Conference on Pattern Recognition (ICPR)
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https://hdl.handle.net/10993/44674
DOI
10.1109/ICPR48806.2021.9412508
 

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ICPR2020_OyedotunShabayekAouadaOttersten.pdf
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Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Disciplines :
Computer science
Author, co-author :
Oyedotun, Oyebade ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Shabayek, Abd El Rahman ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Aouada, Djamila  ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > CVI2
Ottersten, Björn ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
External co-authors :
no
Language :
English
Title :
Revisiting the Training of Very Deep Neural Networks without Skip Connections
Publication date :
2021
Event name :
IEEE 2020 International Conference on Pattern Recognition (ICPR)
Event date :
10-01-2021 to 15-01-2021
FnR Project :
FNR11295431 - Automatic Feature Selection For Visual Recognition, 2016 (01/02/2017-31/01/2021) - Oyebade Oyedotun
Funders :
FNR - Fonds National de la Recherche [LU]
Available on ORBilu :
since 11 November 2020

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