and generative adversarial notes all revolve around exactly what the generator and the discriminator and the learning process are so for generative adverse neural networks the discriminator is just a neural network that we parameterize directly the function D of X is just directly implemented for both noise contrastive estimation and maximum likelihood estimation the discriminator is a ratio between the model that we're learning and the sum of the model density and the generator density so that probably got a