LOGAN: Latent Optimisation for Generative Adversarial Networks

Training generative adversarial networks requires balancing of delicate adversarial dynamics. Even with careful tuning, training may diverge or end up in a bad equilibrium with dropped modes. In this work, we introduce a new form of latent optimisation inspired by the CS-GAN and show that it improves adversarial dynamics by enhancing interactions between the discriminator and the generator. We develop supporting theoretical analysis from the perspectives of differentiable games and stochastic approximation. Our experiments demonstrate that latent optimisation can significantly improve GAN training, obtaining state-of-the-art performance for the ImageNet ( 128 128) dataset. Our model achieves an Inception Score (IS) of 148 and an Fr echet Inception Distance (FID) of 3.4, an improvement of 17% and 32% in IS and FID respectively, compared with the baseline BigGAN-deep model with the same architecture and number of parameters. Generative Adversarial Nets (GANs) are implicit generative models that can be trained to match a given data distribution. GANs were originally developed by Goodfellow et al. (2014) for image data. As the field of generative modelling has advanced, GANs remain at the frontier, generating high-fidelity images at large scale (Brock et al., 2018).