Generative Adversarial Networks (GANs) are a class of machine learning frameworks where two neural networks, a generator and a discriminator, are trained simultaneously through adversarial processes to generate new data with the same statistics as the training set. This innovation has enabled advancements in realistic image synthesis, data augmentation, and unsupervised learning, pushing the boundaries of generative modeling.