DDRM-CapsNet: Capsule Network Based on Deep Dynamic Routing Mechanism for Complex Data

Abstract

The Recently proposed CapsNet has attracted the attention of many researchers. It is a potential alternative to convolutional neural networks (CNNs) and achieves significant increase in performance on some simple datasets like MNIST. However, CapsNet gets a poor performance on more complex datasets like CIFAR-10. To address this problem, we focus on the improvement of the original CapsNet from both the network structure and the dynamic routing mechanism. A new CapsNet architecture aiming at complex data called Capsule Network based on Deep Dynamic Routing Mechanism (DDRM-CapsNet) is proposed. For the purpose of extracting better features, we increase the number of convolutional layers before capsule layer in the encoder. We also improve the dynamic routing mechanism in the original CapsNet by expanding it into two stages and increasing the dimensionality of the final output vector. To verify the efficacy of our proposed network on complex data, we conduct experiments with a single model without using any ensembled methods and data augmentation techniques on five real-world complex datasets. The experimental results demonstrate that our proposed method achieves better accuracy results than the baseline and can still improve the reconstruction performance on the premise of using the same decoder structure as the original CapsNet.