Generative Adversarial Networks (GANs), introduced by Ian Goodfellow in 2014, have revolutionized the field of generative models by enabling the creation of realistic synthetic data across domains such as images, audio, and text. This discussion highlights six notable GAN architectures: CycleGAN, which allows style transformation between images; StyleGAN, known for generating high-resolution images; PixelRNN, which models image probability distributions; text-to-image GANs, designed to create images from textual descriptions; DiscoGAN, which learns cross-domain relations; and lsGAN, which improves image quality using least-squares loss. These architectures utilize the adversarial nature of GANs, involving a generator and a discriminator in a min-max optimization framework, where the generator aims to produce indistinguishable fake samples from real ones, while the discriminator works to differentiate between the two. The article also compares DiscoGAN and CycleGAN, emphasizing their shared use of reconstruction loss but noting differences in loss measurement and parameterization. The GAN's ability to generate data that mimics real-world distributions has led to significant research interest and achievements in machine learning.