Training GANs with predictive projection centripetal acceleration

Although remarkable successful in practice, training generative adversarial networks(GANs) is still quite difficult and iteratively prone to cyclic behaviors, as GANs need to solve a non-convex non-concave min-max game using a gradient descent ascent (GDA) method. Motivated by the ideas of simultaneous centripetal acceleration (SCA) and modified predictive methods (MPM), we propose a novel predictive projection centripetal acceleration (PPCA) methods to alleviate the cyclic behaviors. Besides, under suitable assumptions, we show that the difference between the signed vector of partial derivatives at t + 1 and t is orthogonal to the signed vector of partial derivatives at t for GDA, and the last-iterate exponential convergence on the bilinear game. Finally, numerical simulations are conducted by PPCA in GANs setting, and the results illustrate the effectiveness of our approach.