Deep dive into generator and discriminator networks.
To train the GAN, we need to provide a dataset of real images. In this example, we will use the MNIST dataset, which consists of 70,000 grayscale images of handwritten digits. gans in action pdf github
Since the publication of GANs in Action , the field has evolved. Diffusion models (DALL-E 2, Stable Diffusion, Midjourney) have overtaken traditional GANs for high-fidelity generation. So, is the book still relevant? Deep dive into generator and discriminator networks