learn its own shortcomings and distinguish its own samples from the data and the generative every cell that works approach we constantly update the generator network by following the gradient on its parameters all three of these approaches constantly follow the gradient on the parameters over the discriminator so we can see the way that we get some computational savings relative to maximum likelihood by looking at the corners that both noise contrastive estimation and generative adversarial networks cut for a noise