改进的基于多路径特征的胶囊网络

Improved capsule network based on multipath feature

针对胶囊网络(CapsNet)在复杂数据集上的分类效果差,而且在路由过程中参数数量过大等问题,提出一种基于多路径特征的胶囊网络(MCNet),包含新的胶囊特征提取器和新的胶囊池化方法。该胶囊特征提取器从多个不同路径中并行地提取不同层次、不同位置的特征,然后将特征编码为包含更多语义信息的胶囊特征;胶囊池化方法则在胶囊特征图的每个位置选取最活跃的胶囊,用少量的胶囊表示有效的胶囊特征。在4个数据集(CIFAR-10、SVHN、Fashion-MNIST、MNIST)上与CapsNet等模型进行了对比。实验结果显示,MCNet在CIFAR-10数据集上的分类准确率为79.27%,可训练的参数数量为6.25×10^(6),与CapsNet相比,MCNet的分类准确率提升了8.7%,参数数量减少了46.8%。MCNet能够有效提升分类准确率,同时减少可训练的参数数量。

Concerning the problems of poor classification of Capsule Network(CapsNet)on complex datasets and large number of parameters in the routing process,a Capsule Network based on Multipath feature(MCNet)was proposed,including a novel capsule feature extractor and a novel capsule pooling method.By the capsule feature extractor,the features of different layers and locations were extracted in parallel from multiple paths,and then the features were encoded into capsule features containing more semantic information.In the capsule pooling method,the most active capsules at each position of the capsule feature map were selected,and the effective capsule features were represented by a small number of capsules.Comparisons were performed on four datasets(CIFAR-10,SVHN,Fashion-MNIST,MNIST)with models such as CapsNet.Experimental results show that MCNet has the classification accuracy of 79.27%on CIFAR-10 dataset and the number of trainable parameters of 6.25×10^(6);compared with CapsNet,MCNet has the classification accuracy improved by 8.7%,and the number of parameters reduced by 46.8%.MCNet can effectively improve the classification accuracy while reducing the number of trainable parameters.