关键词: 深度学习, 卷积神经网络, 紧凑型结构, 宽度扩展, 图像识别

Abstract: Aiming at the problems of more and more complex structure of deep convolutional neural network and the large parameters, a new structure of compact convolutional neural network Width-MixedNet and its multi-branch basic module Conv-mixed are designed. The architecture extends the width of the convolutional neural network. The branching structure of Conv-mixed makes multiple different convolutional layers deal with the same feature map and extract different features. In the recognition task of the deep convolutional neural network, the superposition of multiple small convolutional layers is used to reduce the feature maps layer by layer instead of the full connection layers, which is for final feature extraction. The number of parameters of Width-MixedNet is only 3.4×105, which is only 1/30 of the traditional deep convolutional neural networks. Experiments are conducted at CIFAR-10, CIFAR-100 and MNIST with accuracy rates of 93.02%, 66.19% and 99.59%. The experiments show that Width-MixedNet has better performance and learning ability, which observably reduces the parameter size of the network and improves the recognition accuracy.

Key words: deep learning, convolutional neural network, compact structure, width expansion, image recognition