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General Information
    • ISSN: 2010-3700 (Online)
    • Abbreviated Title: Int. J. Mach. Learn. Comput.
    • Frequency: Bimonthly
    • DOI: 10.18178/IJMLC
    • Editor-in-Chief: Dr. Lin Huang
    • Executive Editor:  Ms. Cherry L. Chen
    • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
    • E-mail: ijmlc@ejournal.net
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.

IJMLC 2017 Vol.7(6): 238-242 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2017.7.6.653

Pertinence of Video for Single Image Deep Network

Juliette Chataigner, Stephane Herbin, and Adrien Chan-Hon-Tong
Abstract—Using key frames instead of video to train single image deep neural networks make sense as successive images of one video contain almost the same information. However, we show that using all images can significantly increase performances of deep networks on medium size datasets. Considering, that annotating video can be done much more efficiently than annotating disparate images, we argue that using complete videos should be considered where data are naturally collected this way which is often the case in robotic, autonomous driving, or aerial acquisitions.

Index Terms—Deep learning, video, medium size dataset.

The authors are with Onera the French Aerospace Lab, France (e-mail: adrien.chan_hon_tong@onera.fr).


Cite: Juliette Chataigner, Stephane Herbin, and Adrien Chan-Hon-Tong, "Pertinence of Video for Single Image Deep Network," International Journal of Machine Learning and Computing vol. 7, no. 6, pp. 238-242, 2017.

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