Deep capsule network regularization based on generative adversarial network framework

Abstract

Deep capsule networks have more capsule layers, which makes their performance better on complex images. However, with the increase of layers, overfitting will become more serious. Image reconstruction is an effective regularization method for capsule networks. To improve it, we propose an adversarial decoder that introduces the generative adversarial network framework into the reconstruction process to implement learnable reconstruction losses. This architecture consists of three parts: a deep capsule network, a decoder, and a discriminator. The deep capsule network extracts feature capsules from input images, which are then reconstructed by the decoder. The discriminator is the learnable reconstruction loss function that evaluates the similarity between reconstructed images and input images. Minimizing this learnable reconstruction loss and mean square error of images provides a regularization effect for the deep capsule network. Experimental results show that our models have a competitive performance of regularization on CIFAR10, CIFAR100, and FMNIST.