What is a generative adversarial network?

A generative adversarial network (GAN) is a type of neural network architecture consisting of two models: a generator model that learns to generate new data samples that are similar to a training dataset, and a discriminator model that learns to distinguish between real and generated data samples. The two models are trained in an adversarial manner, where the generator tries to fool the discriminator, and the discriminator tries to correctly identify the real data samples. GANs have been used for tasks such as image and video generation, text generation, and data augmentation.