Domains such as logo synthesis, in which the data has a high degree of
multi-modality, still pose a challenge for generative adversarial networks
(GANs). Recent research shows that progressive training (ProGAN) and mapping
network extensions (StyleGAN) enable both increased training stability for
higher dimensional problems and better feature separation within the embedded
latent space. However, these architectures leave limited control over shaping
the output of the network, which is an undesirable trait in the case of logo
synthesis. This paper explores a conditional extension to the StyleGAN
architecture with the aim of firstly, improving on the low resolution results
of previous research and, secondly, increasing the controllability of the
output through the use of synthetic class-conditions. Furthermore, methods of
extracting such class conditions are explored with a focus on the human
interpretability, where the challenge lies in the fact that, by nature, visual
logo characteristics are hard to define. The introduced conditional style-based
generator architecture is trained on the extracted class-conditions in two
experiments and studied relative to the performance of an unconditional model.
Results show that, whilst the unconditional model more closely matches the
training distribution, high quality conditions enabled the embedding of finer
details onto the latent space, leading to more diverse output.

本文探讨了一种对 StyleGAN 体系结构的条件扩展方法，通过使用合成类别条件提高网络的可控性和结果的分辨率，同时研究了提取这些类别条件的方法以增加人的可解释程度。实验证明，条件模型相对于无条件的模型可以更好的嵌入细节，产生更多样化和高质量的输出结果。