Generative Adversarial Networks and their Miscellaneous Applications: A Review

Generative Adversarial Networks (GANs) were first used to generate images that were similar to images in the data the model was trained on. The GANs training is based on a zero-sum game where the constituent models are adversaries. The mathematical interpretation of GAN training is the mapping of an unknown distribution to the dataset distribution. Future works in the field led to the generation of music, texts, and types of data and GANs still are being explored in scientific, entertainment, fashion, advertising, videogames, and other miscellaneous applications. This review focuses on the versatility of GANs. First, the GAN model is explored with its mathematical intuition. Then come the popular variants of GANs and their applications. Finally, the most recent applications of GANs in different fields are discussed, and the review ends with a discussion of future possible applications of GANs.