  This paper presents Capsule GAN, a Generative adversarial network using
Capsule Network not only in the discriminator but also in the generator.
Recently, Generative adversarial networks (GANs) has been intensively studied.
However, generating images by GANs is difficult. Therefore, GANs sometimes
generate poor quality images. These GANs use convolutional neural networks
(CNNs). However, CNNs have the defect that the relational information between
features of the image may be lost. Capsule Network, proposed by Hinton in 2017,
overcomes the defect of CNNs. Capsule GAN reported previously uses Capsule
Network in the discriminator. However, instead of using Capsule Network,
Capsule GAN reported in previous studies uses CNNs in generator architecture
like DCGAN. This paper introduces two approaches to use Capsule Network in the
generator. One is to use DigitCaps layer from the discriminator as the input to
the generator. DigitCaps layer is the output layer of Capsule Network. It has
the features of the input images of the discriminator. The other is to use the
reverse operation of recognition process in Capsule Network in the generator.
We compare Capsule GAN proposed in this paper with conventional GAN using CNN
and Capsule GAN which uses Capsule Network in the discriminator only. The
datasets are MNIST, Fashion-MNIST and color images. We show that Capsule GAN
outperforms the GAN using CNN and the GAN using Capsule Network in the
discriminator only. The architecture of Capsule GAN proposed in this paper is a
basic architecture using Capsule Network. Therefore, we can apply the existing
improvement techniques for GANs to Capsule GAN.
