A class of machine learning models that consist of two neural networks: a generator and a discriminator. GANs are used for generating new data that resembles a given training dataset. The generator attempts to create synthetic data samples, such as images, audio, or text, that resemble the real data from the training set. The discriminator network, on the other hand, aims to distinguish between real data samples from the training set and the synthetic data samples created by the generator.
