GA-based filter selection for representation in convolutional neural networks

Junbong Kim, Minki Lee, Jongeun Choi, Kisung Seo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One of the deep learning models, a convolutional neural network (CNN) has been very successful in a variety of computer vision tasks. Features of a CNN are automatically generated, however, they can be further optimized since they often require large scale parallel operations and there exist the possibility of overlapping redundant features. The aim of this paper is to use feature selection via evolutionary algorithms to remove the irrelevant deep features. This will minimize the computational complexity and the amount of overfitting while maintaining a good quality of representation. We demonstrate the improvement of the filter representation by performing experiments on three data sets of CIFAR10, metal surface defects, and variation of MNIST and by analyzing the classification performance as well as the variance of the filter.

Original languageEnglish
Title of host publicationComputer Vision – ECCV 2018 Workshops, Proceedings
EditorsLaura Leal-Taixé, Stefan Roth
PublisherSpringer Verlag
Pages609-618
Number of pages10
ISBN (Print)9783030110178
DOIs
Publication statusPublished - 2019
Event15th European Conference on Computer Vision, ECCV 2018 - Munich, Germany
Duration: 2018 Sep 82018 Sep 14

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11132 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other15th European Conference on Computer Vision, ECCV 2018
Country/TerritoryGermany
CityMunich
Period18/9/818/9/14

Bibliographical note

Funding Information:
Acknowledgement. This work was supported by National Research Foundation of Korea Grant funded by the Korea government (NRF-2016R1D1A1A09919650). This work was also funded by the Korea Meteorological Administration Research and Development Program under Grant KMIPA(KMI2018-06710).

Publisher Copyright:
© Springer Nature Switzerland AG 2019.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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