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.
Throughout a variety of experiments, we show that GLO enjoys many of the
desirable properties of GANs: synthesizing visually-appealing samples,
interpolating meaningfully between samples, and performing linear arithmetic
with noise vectors; all of this without the adversarial optimization scheme.

本文介绍了生成对抗网络的成功因素，并提出 “生成潜在优化” 框架来训练深度卷积生成器，实现了可视化合成、样本插值等功能，同时不需要对抗优化方案。