  Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning
have gained popularity because of their ability to represent hierarchical data.
We propose that it is possible to take advantage of the hierarchical
characteristic present in the images by using hyperbolic neural networks in a
GAN architecture. In this study, different configurations using fully connected
hyperbolic layers in the GAN, CGAN, and WGAN are tested, in what we call the
HGAN, HCGAN, and HWGAN, respectively. The results are measured using the
Inception Score (IS) and the Fr\'echet Inception Distance (FID) on the MNIST
dataset. Depending on the configuration and space curvature, better results are
achieved for each proposed hyperbolic versions than their euclidean
counterpart.
