Gans theory

GANs, or Generative Adversarial Networks, are a class of machine learning frameworks introduced by Ian Goodfellow and his colleagues in 2014. The fundamental idea behind GANs is to set up a game between two models: a generator and a discriminator. 1. **Generator**: This model generates new data instances. It takes random noise as input and tries to produce data that mimics the actual distribution of the training data.