Title: Optimizing the Latent Space of Generative Networks,
Abstract: Generative Adversarial Networks (GANs) have been shown to be able to sample
impressively realistic images. GAN training consists of a saddle point
optimization problem that can be thought of as an adversarial game between a
generator which produces the images, and a discriminator, which judges if the
images are real. Both the generator and the discriminator are commonly
parametrized as deep convolutional neural networks. The goal of this paper is
to disentangle the contribution of the optimization procedure and the network
parametrization to the success of GANs. To this end we introduce and study
Generative Latent Optimization (GLO), a framework to train a generator without
the need to learn a discriminator, thus avoiding challenging adversarial
optimization problems. We show experimentally that GLO enjoys many of the
desirable properties of GANs: learning from large data, synthesizing
visually-appealing samples, interpolating meaningfully between samples, and
performing linear arithmetic with noise vectors.