Image Classification Using Convolutional Neural Networks with Different Convolution Operations

Abstract

In this paper, three types of convolution operations in convolutional neural networks (CNNs) are studied including regular convolution, separable convolution and group convolution. For regular convolution case, the modified VGG-19 is used to construct the deep networks. For separable convolution case, the MobileNet is applied to build deep model. For group convolution, the VGG-like plain network is used to construct the model. The experimental results of image classification on the CIFAR-10 and Flower102 datasets are used to evaluate the performance of CNNs and to demonstrate which convolution operation is a better choice according to accuracy and complexity.