The Evolution of Generative Adversarial Networks: From GAN to StyleGAN-3

Generative Adversarial Networks (GANs) have revolutionized the field of artificial intelligence since their introduction by Ian Goodfellow and his colleagues in 2014. These networks consist of two neural networks—a generator and a discriminator—that are trained simultaneously through adversarial training. The original GAN architecture introduced a novel approach to generative modeling, but faced challenges like training instability and mode collapse. Progressive GAN addressed many limitations by employing a training methodology where both networks start with low-resolution images and gradually add layers. StyleGAN further improved control over generated images by separating high-level attributes from stochastic variations. StyleGAN-2 refined the architecture by addressing artifacts, while StyleGAN-3 focused on eliminating "texture sticking" for more natural movement of features.