There are many papers on GANs, each proposing new novel architectures and tweaks; however, most of them are at least somewhat based on the Deep Convolutional GAN (DCGAN). For the rest of the chapter, we will be focusing on this model because this knowledge will hopefully serve you well as you take on new and exciting GAN architectures that aren't covered here, such as the Conditional GAN (cGAN), the Stack GAN, the InfoGAN, or the Wasserstein GAN, or possibly some other new variant that you might choose to look at next.
The DCGAN was introduced by Alex Radford, Luke Metz, and Soumith Chintala in the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (https://arxiv.org/pdf/1511.06434.pdf ...
