Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm designed to solve the problem of generative modeling. The primary goal of a generative model is to learn the probability distribution that generated a set of training examples and then use this learned distribution to generate new examples. GANs achieve this by engaging in a game between two neural networks: the generator and the discriminator. The generator aims to create realistic samples from the model distribution, while the discriminator tries to distinguish between real samples from the training set and fake samples generated by the generator. This competition drives the generator to improve its ability to produce realistic samples, leading to more accurate and diverse outputs. GANs have been particularly successful in generating high-resolution images and have found applications in various tasks, including image generation, domain adaptation, and interactive digital media effects. However, training GANs remains challenging due to the non-convex nature of the optimization problem and the difficulty in finding stable Nash equilibria. Despite these challenges, GANs have shown great potential and continue to be an active area of research, with ongoing efforts to improve their convergence properties and reliability.Generative Adversarial Networks (GANs) are a type of artificial intelligence algorithm designed to solve the problem of generative modeling. The primary goal of a generative model is to learn the probability distribution that generated a set of training examples and then use this learned distribution to generate new examples. GANs achieve this by engaging in a game between two neural networks: the generator and the discriminator. The generator aims to create realistic samples from the model distribution, while the discriminator tries to distinguish between real samples from the training set and fake samples generated by the generator. This competition drives the generator to improve its ability to produce realistic samples, leading to more accurate and diverse outputs. GANs have been particularly successful in generating high-resolution images and have found applications in various tasks, including image generation, domain adaptation, and interactive digital media effects. However, training GANs remains challenging due to the non-convex nature of the optimization problem and the difficulty in finding stable Nash equilibria. Despite these challenges, GANs have shown great potential and continue to be an active area of research, with ongoing efforts to improve their convergence properties and reliability.