 The generative network's training objective is to increase the error rate of the discriminative network (i.e., "fool" the discriminator network by producing novel candidates that the discriminator thinks are not synthesized (are part of the true data distribution)).A known dataset serves as the initial training data for the discriminator.Training it involves presenting it with samples from the training dataset, until it achieves acceptable accuracy.