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arXiv · 2003.10847

Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets

Viktor Varkarakis·Shabab Bazrafkan·Peter Corcoran
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Abstract

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.

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Viktor Varkarakis, Shabab Bazrafkan, Peter Corcoran. 2020-03-24. Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets. https://arxiv.org/abs/2003.10847

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