Implemented the Style GAN Architecture as proposed in this paper.
- It consists of a mapping network
$f$ to learn a disentangled latent space. A disentangled latent space enables the possibility to find direction vectors that correspond to indvidual factors of variation. - The synthesis netwok
$g$ performs style-mixing. Further, we add random Gaussian Noise to each block as a means to generate stochastic detail. - Adaptive Instance Normalization is used in each block due to its efficiency and compact representation.