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 language | English |
---|---|
Pages (from-to) | 161-176 |
Number of pages | 16 |
Journal | Communications for Statistical Applications and Methods |
Volume | 29 |
Issue number | 2 |
DOIs | |
Publication status | Published - 2022 Mar |
Bibliographical note
Funding Information:Yoonsuh Jung’s work was partially supported by National Research Foundation of Korea (NRF) grant funded by Korea government (MIST)(2019R1A4A1028134 and 2021R1F1A1062347).
Funding Information:
Jung’s work has been partially supported by National Research Foundation of Korea (NRF) grants funded by the Korea government(MIST) 2019R1A4A1028134 and 2021R1F1A1062347. 1Corresponding author: Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, South Korea. E-mail: [email protected]
Publisher Copyright:
© 2022. The Korean Statistical Society, and Korean International Statistical Society. All rights reserved
Keywords
- Classification
- ImageNet large-scale visual recog nition challenge (ILSVRC)
- convolutional neural network (CNN)
- image data
ASJC Scopus subject areas
- Statistics and Probability
- Modelling and Simulation
- Finance
- Statistics, Probability and Uncertainty
- Applied Mathematics