Stylized Projected GAN: A Novel Architecture for Fast and Realistic
  Image Generation
Md Nurul Muttakin Malik Shahid Sultan Robert Hoehndorf Hernando Ombao
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.