doi: 10.18178/ijmlc.2019.9.6.881
CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification
Xuefeng Jiang1
,
Yikun Wang1 ,
Wenbo Liu1 ,
Shuying Li2 ,
Junrui Liu1
- 1School of Computing, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, P.R. China
- 2School of Automation, Xi'an University of Posts & Telecommunications, Xi'an 710121, Shaanxi, P.R. China
- Manuscript receivedAugust 25, 2019
- revisedOctober 12, 2019
Abstract
Image classification is one of the predominant tasks in computer vision. So far, there are many approaches in image classification, and the most typical methods are Convolutional Neural Networks (CNN), BOF-based algorithms, etc. Most of these methods have a good performance, but there are still some limitations. Capsule Network (CapsNet) is the most advanced algorithm, which realizes the operation based on active vector and dynamic routing, and can overcome limitations of the original algorithm. This paper attempts to apply CapsNet into image classification as well as another two efficient classification methods, which are CNN and Fully Convolutional Network (FCN). We use two datasets: MNIST and CIFAR-10 to train our model and tested the networks. Finally, compare and evaluate their performances in aspects of time cost, loss, accuracy and the number of parameters.
Keywords
- Capsule network
- convolutional neural networks
- full convolutional neural network
- image classification
How to Cite
Xuefeng Jiang, Yikun Wang, Wenbo Liu, Shuying Li, and Junrui Liu, "CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification," International Journal of Machine Learning, vol. 9, no. 6, pp. 840-848, 2026. https://doi.org/10.18178/ijmlc.2019.9.6.881
Copyright & License
Copyright © 2026 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).