[en] This study investigates the inability of two popular data splitting techniques: train/test split and k-fold cross-validation that are to create training and validation data sets, and to achieve sufficient generality for supervised deep learning (DL) methods. This failure is mainly caused by their limited ability of new data creation. In response, the bootstrap is a computer based statistical resampling method that has been used efficiently for estimating the distribution of a sample estimator and to assess a model without having knowledge about the population. This paper couples cross-validation and bootstrap to have their respective advantages in view of data generation strategy and to achieve better generalization of a DL model. This paper contributes by: (i) developing an algorithm for better selection of training and validation data sets, (ii) exploring the potential of bootstrap for drawing statistical inference on the necessary performance metrics (e.g., mean square error), and (iii) introducing a method that can assess and improve the efficiency of a DL model. The proposed method is applied for semantic segmentation and is demonstrated via a DL based classification algorithm, PointNet, through aerial laser scanning point cloud data.
Centre de recherche :
ULHPC - University of Luxembourg: High Performance Computing
Disciplines :
Ingénierie, informatique & technologie: Multidisciplinaire, généralités & autres
Auteur, co-auteur :
NURUNNABI, Abdul Awal Md ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
TEFERLE, Felix Norman ; University of Luxembourg > Faculty of Science, Technology and Medicine (FSTM) > Department of Engineering (DoE)
Laefer, Debra; 2Center for Urban Science and Progress ; Department of Civil and Urban Engineering > New York University, USA
Remondino, Fabio; 33D Optical Metrology (3DOM) unit > Bruno Kessler Foundation (FBK), Trento, Italy
Karas, Ismail Rakip; Department of Computer Engineering > Karabuk University, Karabuk, Turkey
Li, Jonatha; 5Geography and Environmental Management > University of Waterloo, Canada
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
kCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection
Date de publication/diffusion :
octobre 2022
Nom de la manifestation :
The 7th Smart City Applications, International Conference
Lieu de la manifestation :
Castelo Branco, Portugal
Date de la manifestation :
from 19-10-2022 to 21-10-2022
Manifestation à portée :
International
Titre de l'ouvrage principal :
kCV-B: Bootstrap with Cross-Validation for Deep Learning Model Development, Assessment and Selection
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