Revisiting the Training of Very Deep Neural Networks without Skip Connections

Reference : Revisiting the Training of Very Deep Neural Networks without Skip Connections


	Document type :	Scientific congresses, symposiums and conference proceedings : Poster
	Discipline(s) :	Engineering, computing & technology : Computer science
	To cite this reference:	http://hdl.handle.net/10993/44674

Title :	Revisiting the Training of Very Deep Neural Networks without Skip Connections
Language :	English
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) > >]
Publication date :	2021
Peer reviewed :	Yes
Event name :	IEEE 2020 International Conference on Pattern Recognition (ICPR)
Event date :	10-01-2021 to 15-01-2021
Research centres :	Interdisciplinary Centre for Security, Reliability and Trust (SnT) > SIGCOM
Funders :	Fonds National de la Recherche - FnR
Permalink :	http://hdl.handle.net/10993/44674
FnR project :	FnR ; FNR11295431 > Oyebade Oyedotun > > Automatic Feature Selection for Visual Recognition > 01/02/2017 > 31/01/2021 > 2016

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	File	Commentary	Version	Size	Access
Open access	ICPR2020_OyedotunShabayekAouadaOttersten.pdf		Author preprint	2.02 MB	View/Open

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