CapsNet, CNN, FCN: Comparative Performance Evaluation for Image Classification

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.