Generative Adversarial Networks (GANS)

Generative Adversarial Networks (GANs) represent a breakthrough in the field of deep learning, enabling the generation of high-quality synthetic data. GANs consist of two neural networks, a generator and a discriminator, that compete against each other in a game-theoretic framework. This paper provides an overview of GANs, their architecture, applications, and recent advancements. We discuss various types of GANs, their practical applications in areas such as image synthesis, data augmentation, and machine learning, and present methodologies for improving their training and performance. The paper concludes with challenges in the GAN domain and future directions for research.