StyleGAN is a GAN formulation which is capable of generating very high-resolution images even of 10241024 resolution. The idea is to build a stack of layers where initial layers are capable of generating low-resolution images (starting from 22) and further layers gradually increase the resolution.
The easiest way for GAN to generate high-resolution images is to remember images from the training dataset and while generating new images it can add random noise to an existing image. In reality, StyleGAN doesn’t do that rather it learn features regarding human face and generates a new image of the human face that doesn’t exist in reality. If this sounds interesting, visit https://thispersondoesnotexist.com/ Each visit to this URL will generate a new image of a human face who doesn’t exist in the universe.
In StyleGAN, the latent space vector z is passed through a mapping transformation comprises of 8 fully connected layers whereas the synthesis network comprises of 18 layers, where each layer produces image from 4 x 4 to 1024 x 1024. The output layer output RGB image through a separate convolution layer. This architecture has 26.2 million parameters and because of this very high number of trainable parameters, this model requires a huge number of training images to build a successful model. Each layer is normalized using Adaptive instance normalization where each feature map is normalized separately, and then scaled and biased using the corresponding scalar components from style y. Thus the dimensionality of y is twice the number of feature maps on that layer.