RS-CapsNet: An Advanced Capsule Network
Shuai Yang, Feifei Lee, Ran Miao, Jiawei Cai, Lu Chen, Wei Yao, Koji Kotani, Qiu Chen
Abstract
Capsule Network is a novel and promising neural network in the field of deep learning, which has shown good performance in image classification by encoding features into capsules and constructing the part-whole relationships. However, the original Capsule Network is not suitable for the images with complex background due to its weak ability of feature extraction, a large number of training parameters, and the characteristic of explaining everything in the image. To address the above issues, we propose an advanced Capsule Network named RS-CapsNet, which uses Res2Net block to extract multi-scale features, and Squeeze-and-Excitation (SE) block to highlight useful features and suppress useless features. Meanwhile, we adopt the method of linear combination between capsules, which enhances the representation ability of capsules for detected object and reduces the number of capsules. Moreover, we propose the method of firstly constructing intermediate capsules that can represent most of the detected object, and then using the intermediate capsules and primary capsules to construct the classification capsules together. The experimental results show that proposed RS-CapsNet has better performance on CIFAR10, CIFAR100, SVHN, FashionMNIST, and AffNIST datasets, it can also provide better translation equivariance, and the number of training parameters is reduced by 65.11%.