Understanding GAN-based image synthesis

Deep generative models have been shown to produce photorealistic 2D images when trained on a distribution from a particular domain. Generative Adversarial Networks (GANs) are one of the most widely used frameworks for this purpose. They can synthesize high-quality photorealistic images at resolutions of 1,024 x 1,024 and beyond. For example, they have been used to generate realistic faces:

Figure 7.1: Randomly generated faces as high-quality 2D images using StyleGAN2

GANs can be trained to generate similar-looking images from any data distribution. The same StyleGAN2 model, when trained on a car dataset, can generate high-resolution images of cars:

Figure 7.2: Randomly generated cars as 2D images using StyleGAN2

GANs are based on a game-theoretic scenario where a generator neural network generates an image. However, in order to be successful, it must...