  StyleGAN is a state-of-art generative adversarial network architecture that
generates random 2D high-quality synthetic facial data samples. In this paper,
we recap the StyleGAN architecture and training methodology and present our
experiences of retraining it on a number of alternative public datasets.
Practical issues and challenges arising from the retraining process are
discussed. Tests and validation results are presented and a comparative
analysis of several different re-trained StyleGAN weightings is provided 1. The
role of this tool in building large, scalable datasets of synthetic facial data
is also discussed.
