Communications for Statistical Applications and Methods

  • 제29권2호
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  • Pages.161-176
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  • 2022
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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A review and comparison of convolution neural network models under a unified framework

  • 투고 : 2021.08.11
  • 심사 : 2021.12.08
  • 발행 : 2022.03.31
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초록

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.

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과제정보

Yoonsuh Jung's work was partially supported by National Research Foundation of Korea (NRF) grant funded by Korea government (MIST)(2019R1A4A1028134 and 2021R1F1A1062347).

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