Style GAN

• builds the picture layer after layer, where the Layers get bigger and more accurate
• For example, the first layer is 4 by 4 pixels, the second 8 by 8, and so on
• every new layer can benefit from the less granular results of the previous ones
• better separate the generator and the discriminator, which ensures less dependence of the generator on the training set
• his allows one to, for example, reduce discrimination in the generated pictures