Generative Adversarial Networks

Generative Adversarial Networks (GANs) are a type of neural network architecture designed to generate new data that resembles the data they were trained on. They consist of two networks: a generator, which creates new data instances, and a discriminator, which evaluates the authenticity of the generated data. These two networks are trained in an adversarial process, where the generator tries to fool the discriminator, and the discriminator tries to distinguish between real and generated data. This competition drives both networks to improve, ultimately leading the generator to produce highly realistic data.