Optimizing the Latent Space of Generative Networks

Understand

Generative Adversarial Networks (GANs) have achieved remarkable results in the task of generating realistic natural images.

• In most successful applications, GAN models share two common aspects: solving a challenging saddle point optimization problem, interpreted as an adversarial game between a generator and a discriminator functions; and parameterizing the generator and the discriminator as deep convolutional neural networks.
• The goal of this paper is to disentangle the contribution of these two factors to the success of GANs.
• In particular, we introduce Generative Latent Optimization (GLO), a framework to train deep convolutional generators using simple reconstruction losses.