In this section, we will learn how to implement a Generator and Discriminator that is commonly used in the GAN framework. Within the possible network architectures, our research community has focused on network architectures similar to DCGAN and Resnet. The DCGAN architecture was first described in the paper Unsupervised Representation Learning with Deep Convolutional by A. Radford et al. Resnet-like GAN architectures were probably first used in the Wasserstein GAN paper by Martin Arjovsky et al and first described in Deep Residual Learning for Image Recognition by K. He et al.
At the time Resnets were proposed, Resnets and their residual connections were essential for achieving state-of-the-art results in computer vision tasks. The concept of residual connections extended to other architectures and domains, and has been extensively used...