What is a generative adversarial network?

A Generative Adversarial Network (GAN) is a type of machine learning framework used for generating new data samples that resemble a given dataset. It consists of two neural networks, called the generator and the discriminator, which compete against each other. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. Through this adversarial process, GANs can learn to produce highly realistic images, audio, and other types of data. They are widely used in applications like image synthesis, style transfer, and data augmentation.