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

Viktor Varkarakis, S. Bazrafkan, P. Corcoran
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引用次数: 3

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. The role of this tool in building large, scalable datasets of synthetic facial data is also discussed.
重新训练StyleGAN -迈向构建大型,可扩展的合成面部数据集的第一步
StyleGAN是一种最先进的生成对抗网络架构,可以生成随机的2D高质量合成面部数据样本。在本文中,我们概述了StyleGAN的架构和训练方法,并介绍了我们在一些可供选择的公共数据集上对其进行再训练的经验。讨论了再培训过程中出现的实际问题和挑战。给出了测试和验证结果,并对几种不同的重新训练的StyleGAN权重进行了比较分析。本文还讨论了该工具在构建大型、可扩展的合成面部数据集中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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