Abstract: Capsule Network is a new type of deep neural network that uses vectors to express information of image feature and overcomes two major problems of convolutional neural networks by introducing dynamic routing algorithms.First,convolutional neural networks cannot learn and express the part-whole relationship of images.Second,pooling operations lead to serious loss of image feature information.However,CapsNet needs to learn all the features of the image,and when the image background is complex,it has the problems of insufficient information of extracted image features,large number of training parameters and low training efficiency.To this end,firstly,a lightweight image feature extractor RA module is designed to extract image feature information faster and more completely.Secondly,two different depths of lightweight branches are designed to improve the training efficiency of the network.Finally,a new compression function hc-squash is designed to ensure that the network can acquire more useful information,and a multi-branch RA (Resnet Attention) capsule network is proposed.Through the application in the four image classification datasets of MNIST,Fashion-MNIST,affNIST and CIFAR-10,it is confirmed that the multi-branch RA capsule network outperforms CapsNet and MLCN in several performance metrics,and an improvement scheme is designed for the proposed network to achieve optimised classification performance.

Key words: Attention mechanism, Capsule network, Deep learning, Resnet attention module, Squash function