The discriminator model

 This model represents a differentiable function that tries to maximize a probability of 1 for samples drawn from the training distribution. This can be any classification model, but we usually prefer a deep neural network for this. This is the throw-away model(similar to the decoder part of autoencodeers).

The discriminator is also used to classify whether the output from the generator is real or fake. The main utility of this model is to help develop a robust generator. We denote the discriminator model as D and its output as D(x). When it is used to classify output from the generator model. the discriminator model is denoted as D(G(z)), where G(z) is the output from the generator model.