A review and comparison of convolution neural network models under a unified framework

Jimin Park, Yoonsuh Jung

Research output: Contribution to journalArticlepeer-review

Abstract

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.

Original languageEnglish
Pages (from-to)161-176
Number of pages16
JournalCommunications for Statistical Applications and Methods
Volume29
Issue number2
DOIs
Publication statusPublished - 2022 Mar

Keywords

  • Classification
  • convolutional neural network (CNN)
  • image data
  • ImageNet large-scale visual recog nition challenge (ILSVRC)

ASJC Scopus subject areas

  • Statistics and Probability
  • Modelling and Simulation
  • Finance
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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