Generative adversarial network optimization

Abstract

A process for discovering optimal Generative Adversarial Networks (GAN) includes jointly optimizing the three functions of a GANs process including (i) a real component of a discriminator network's loss that is a function of D(x), wherein D(x) is the discriminator network's output for a real sample from an input dataset; (ii) a synthetic component of the discriminator network's loss that is a function of D(G(z)), wherein D(G(z)) is the discriminator network's output for a generator network's synthetic samples z from a latent distribution; and (iii) a generator network's loss which is a function of D(G(z)), with the discriminator network's total loss being the sum of components (i) and (ii). And separately optimizing each of the three functions in accordance with a process for evolving candidate loss functions having tree format by a genetic algorithm and optimizing selected candidate loss functions by optimizing set of coefficients of each of the one or more best candidate loss functions, wherein the set of coefficients is represented by a vector with dimensionality equal to the number of nodes in the best candidate loss function's tree.