Generative Adversarial Networks are used for generating the data using a
generator and a discriminator, GANs usually produce high-quality images, but
training GANs in an adversarial setting is a difficult task. GANs require high
computation power and hyper-parameter regularization for converging. Projected
GANs tackle the training difficulty of GANs by using transfer learning to
project the generated and real samples into a pre-trained feature space.
Projected GANs improve the training time and convergence but produce artifacts
in the generated images which reduce the quality of the generated samples, we
propose an optimized architecture called Stylized Projected GANs which
integrates the mapping network of the Style GANs with Skip Layer Excitation of
Fast GAN. The integrated modules are incorporated within the generator
architecture of the Fast GAN to mitigate the problem of artifacts in the
generated images.

利用转移学习将生成和真实样本投影到预训练的特征空间中，提出了优化后的结构化投影生成式对抗网络 Stylized Projected GANs，用于解决生成图像中出现的问题。