Generative adversarial networks (GANs) evolved into one of the most successful unsupervised techniques for generating realistic images. Even though it has recently been shown that GAN training converges, GAN models often end up in local Nash equilibria that are associated with mode collapse or otherwise fail to model the target distribution. We introduce Coulomb GANs, which pose the GAN learning problem as a potential field of charged particles, where generated samples are attracted to training set samples but repel each other. The discriminator learns a potential field while the generator decreases the energy by moving its samples along the vector (force) field determined by the gradient of the potential field. Through decreasing the energy, the GAN model learns to generate samples according to the whole target distribution and does not only cover some of its modes. We prove that Coulomb GANs possess only one Nash equilibrium which is optimal in the sense that the model distribution equals the target distribution. We show the efficacy of Coulomb GANs on a variety of image datasets. On LSUN and celebA, Coulomb GANs set a new state of the art and produce a previously unseen variety of different samples.

翻译：生成对抗性网络(GANs) 演变成最成功且不受监督的生成现实图像的技术之一。 尽管最近显示GAN培训汇集, GAN模型往往最终出现在当地Nash 平衡中,与模式崩溃有关,或者无法模拟目标分布。 我们引入了Coulomb GANs, 造成GAN学习问题, 作为充电颗粒的潜在领域, 生成的样本被吸引用于培训集成样本, 但相互反射。 导师学习了一个潜在的字段, 而生成器则通过将样本移入由潜在字段的梯度决定的矢量( 力) 字段来降低能量。 通过减少能量, GAN 模型学会了根据整个目标分布生成样本, 而不只覆盖其某些模式。 我们证明 Coulomb GANs 仅拥有一种纳什平衡, 其最理想的就是模型分布等于目标分布。 我们在各种图像数据集上展示了库伦戈 GANs 的功效。 在 LSUN 和 CelebA 上, Coulomb GANs 设置了新的艺术样品, 并制作了一种不同的视觉样品。