  Recent work has shown significant progress in the direction of synthetic data
generation using Generative Adversarial Networks (GANs). GANs have been applied
in many fields of computer vision including text-to-image conversion, domain
transfer, super-resolution, and image-to-video applications. In computer
vision, traditional GANs are based on deep convolutional neural networks.
However, deep convolutional neural networks can require extensive computational
resources because they are based on multiple operations performed by
convolutional layers, which can consist of millions of trainable parameters.
Training a GAN model can be difficult and it takes a significant amount of time
to reach an equilibrium point. In this paper, we investigate the use of
depthwise separable convolutions to reduce training time while maintaining data
generation performance. Our results show that a DepthwiseGAN architecture can
generate realistic images in shorter training periods when compared to a
StarGan architecture, but that model capacity still plays a significant role in
generative modelling. In addition, we show that depthwise separable
convolutions perform best when only applied to the generator. For quality
evaluation of generated images, we use the Fr\'echet Inception Distance (FID),
which compares the similarity between the generated image distribution and that
of the training dataset.
