Before revision: We propose a variant of the standard min-max framework for GANs to learn a distribution, where the discriminator can update its strategy in a greedy manner until it reaches a first-order stationary point.
After revision: We propose a variant of the standard min-max framework for GANs to learn a distribution, where the discriminator can update its strategy in a greedy manner until it reaches a *first-order* stationary point.