  Similar to the majority of deep learning applications, diagnosing skin
diseases using computer vision and deep learning often requires a large volume
of data. However, obtaining sufficient data for particular types of facial skin
conditions can be difficult due to privacy concerns. As a result, conditions
like Rosacea are often understudied in computer-aided diagnosis. The limited
availability of data for facial skin conditions has led to the investigation of
alternative methods for computer-aided diagnosis. In recent years, Generative
Adversarial Networks (GANs), mainly variants of StyleGANs, have demonstrated
promising results in generating synthetic facial images. In this study, for the
first time, a small dataset of Rosacea with 300 full-face images is utilized to
further investigate the possibility of generating synthetic data. The
preliminary experiments show how fine-tuning the model and varying experimental
settings significantly affect the fidelity of the Rosacea features. It is
demonstrated that $R_1$ Regularization strength helps achieve high-fidelity
details. Additionally, this study presents qualitative evaluations of
synthetic/generated faces by expert dermatologists and non-specialist
participants. The quantitative evaluation is presented using a few validation
metric(s). Furthermore a number of limitations and future directions are
discussed. Code and generated dataset are available at:
\url{https://github.com/thinkercache/stylegan2-ada-pytorch}
