GANs (Generative Adversarial Networks) are a type of neural network architecture that are designed to generate new data that is similar to some existing data. They consist of two parts: a generator network and a discriminator network.
The generator network is responsible for creating new data that mimics the distribution of some existing data. The discriminator network is trained to distinguish between the generated data and the real data. The two networks are trained together in a process called adversarial training, where the generator tries to generate data that will fool the discriminator, and the discriminator tries to correctly identify the real data from the generated data.
Through this process of feedback and competition, the generator gradually learns to generate data that is increasingly similar to the real data. GANs have been used for a variety of applications, such as generating realistic images, synthesizing speech and music, and creating new text.