Hyper-GAN: Transferring Unconditional to Conditional GANs with HyperNetworks

Abstract

Conditional GANs have matured in recent years and are able to generate high-quality realistic images. However, the computational resources and the training data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. In this paper, we explore the transfer from high-quality pre-trained unconditional GANs to conditional GANs. To this end, we propose hypernetwork-based adaptive weight modulation. In addition, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the knowledge transfer, we propose to use a self-supervised (contrastive) loss to improve the GAN discriminator. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on several standard benchmarks.