How to Train StyleGAN to Generate Realistic Faces

Generative Adversarial Networks (GAN) is an architecture introduced by Ian Goodfellow and his colleagues in 2014 for generative modeling, which is using a model to generate new samples that imitate an existing dataset. It is composed of two networks: the generator that generates new samples, and the discriminator that detects fake samples. The generator tries to fool the discriminator while the discriminator tries to detect samples synthesized by the generator. Once trained, the generator can be used to create new samples on demand. GANs have quickly become popular due to their various interesting applications such as style transfer, image-to-image translation or video generation.