论文标题
重新训练样式 - 迈向构建大型，可扩展的合成面部数据集的第一步
Re-Training StyleGAN -- A First Step Towards Building Large, Scalable Synthetic Facial Datasets
论文作者
论文摘要
StyleGan是一种最先进的生成对抗网络体系结构，可生成随机的2D高质量合成面部数据样本。在本文中，我们回顾了StyleGAN架构和培训方法，并介绍了我们在许多替代公共数据集上重新训练它的经验。讨论了再培训过程引起的实际问题和挑战。提出了测试和验证结果，并提供了几种不同训练的样式权重的比较分析1。还讨论了该工具在构建合成面部数据的大型，可扩展的数据集中的作用。
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.
