I agree that this site is using cookies. You can find further informations
here
.
X
Login
Merkliste (
0
)
Home
About us
Home About us
Our history
Profile
Press & public relations
Friends
The library in figures
Exhibitions
Projects
Training, internships, careers
Films
Services & Information
Home Services & Information
Lending and interlibrary loans
Returns and renewals
Training and library tours
My Account
Library cards
New to the library?
Download Information
Opening hours
Learning spaces
PC, WLAN, copy, scan and print
Catalogs and collections
Home Catalogs and Collections
Rare books and manuscripts
Digital collections
Subject Areas
Our sites
Home Our sites
Central Library
Law Library (Juridicum)
BB Business and Economics (BB11)
BB Physics and Electrical Engineering
TB Engineering and Social Sciences
TB Economics and Nautical Sciences
TB Music
TB Art & Design
TB Bremerhaven
Contact the library
Home Contact the library
Staff Directory
Open access & publishing
Home Open access & publishing
Reference management: Citavi & RefWorks
Publishing documents
Open Access in Bremen
zur Desktop-Version
Toggle navigation
Merkliste
1 Ergebnisse
1
IDEnet : Inception-Based Deep Convolutional Neural Network ..:
, In:
2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
,
Cahyawijaya, Samuel
;
Wilie, Bryan
;
Adiprawita, Widyawardana
- p. 548-553 , 2018
Link:
https://doi.org/10.1109/EECSI.2018.8752637
RT T1 <cTitle>2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)</cTitle>: T1 <Title>IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation</Title> UL https://suche.suub.uni-bremen.de/peid=ieee-8752637&Exemplar=1&LAN=DE A1 Cahyawijaya, Samuel A1 Wilie, Bryan A1 Adiprawita, Widyawardana YR 2018 K1 Feature extraction K1 Convolution K1 Task analysis K1 Computational modeling K1 Adaptation models K1 Head K1 Adaptive systems K1 crowd counting K1 inception network K1 convolutional neural network K1 deep learning K1 dense aware K1 scale adaptive SP 548 OP 553 LK http://dx.doi.org/https://doi.org/10.1109/EECSI.2018.8752637 DO https://doi.org/10.1109/EECSI.2018.8752637 SF ELIB - SuUB Bremen
Export
RefWorks (nur Desktop-Version!)
Flow
(Zuerst in
Flow
einloggen, dann importieren)
