摘要：A capsule network (CapsNet) is a recently proposed neural network model with a new structure. The purpose of CapsNet is to form activation capsules. In this paper, our team proposes a dual attention mechanism capsule network (DA-CapsNet). In DA-CapsNet, the first layer of the attention mechanism is added after the convolution layer and is referred to as Conv-Attention; the second layer is added after the PrimaryCaps and is referred to as Caps-Attention. The experimental results show that DA-CapsNet performs better than CapsNet. For MNIST, the trained DA-CapsNet is tested in the testset, the accuracy of the DA-CapsNet is 100% after 8 epochs, compared to 25 epochs for CapsNet. For SVHN, CIFAR10, FashionMNIST, smallNORB, and COIL-20, the highest accuracy of DA-CapsNet was 3.46%, 2.52%, 1.57%, 1.33% and 1.16% higher than that of CapsNet. And the results of image reconstruction in COIL-20 show that DA-CapsNet has a more competitive performance than CapsNet.
