  In this paper, we propose Generative Adversarial Network (GAN) architectures
that use Capsule Networks for image-synthesis. Based on the principal of
positional-equivariance of features, Capsule Network's ability to encode
spatial relationships between the features of the image helps it become a more
powerful critic in comparison to Convolutional Neural Networks (CNNs) used in
current architectures for image synthesis. Our proposed GAN architectures learn
the data manifold much faster and therefore, synthesize visually accurate
images in significantly lesser number of training samples and training epochs
in comparison to GANs and its variants that use CNNs. Apart from analyzing the
quantitative results corresponding the images generated by different
architectures, we also explore the reasons for the lower coverage and diversity
explored by the GAN architectures that use CNN critics.
