Reference : Software Engineering for Dataset Augmentation using Generative Adversarial Networks
Scientific congresses, symposiums and conference proceedings : Unpublished conference
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/39783
Software Engineering for Dataset Augmentation using Generative Adversarial Networks
English
Jahic, Benjamin mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Guelfi, Nicolas mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
Ries, Benoît mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Computer Science and Communications Research Unit (CSC) >]
19-Oct-2019
8
Yes
International
10th IEEE International Conference on Software Engineering and Service Science
from 18-10-2019 to 20-10-2019
Beijing
China
[en] software engineering ; development process ; dataset engineering ; automated data generation ; neural network training
[en] Software engineers require a large amount of data for building neural network-based software systems. The engineering of these data is often neglected, though, it is a critical and time-consuming activity. In this work, we present a novel software engineering approach for dataset augmentation using neural networks. We propose a rigorous process for generating synthetic data to improve the training of neural networks. Also, we demonstrate our approach to successfully improve the recognition of handwritten digits using conditional generative
adversarial networks (cGAN). Finally, we shortly discuss selected important issues of our process, presenting related work and proposing some improvements.
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/39783

File(s) associated to this reference

Fulltext file(s):

FileCommentaryVersionSizeAccess
Open access
icsess_065_camera_ready.pdfPublisher postprint776.71 kBView/Open

Bookmark and Share SFX Query

All documents in ORBilu are protected by a user license.