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 optimization framework where the max-player is constrained to update the maximization variable in a greedy manner until it reaches a first-order stationary point.