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→Generative Adversarial Networks
===Generative Adversarial Networks===
GANs are a particular typology type of generative model used for unsupervised learning, which attempt to synthesize new data that is indistinguishable from the training data i.e. with the same distribution function of original data.
It uses two NNs, specifically two CNNs in the most recent approaches, which are locked in a competition game: a generator, which is fed a vector of random numbers i.e. the latent vector and outputs synthesized data i.e. the generated images, and a discriminator, which is fed a batch of data, in this case a batch of images, and outputs a prediction of it being from the training set or from the generated set, basically learning a binary classification problem. In other words, the generator creates fake data and the discriminator attempts to distinguish these fakes samples from the real ones.
