TL;DR通过训练 GAN 在人工数据集上,我们发现混合多个 GANs 相比于使用更深、更宽、更复杂的单个生成器会获得更好的结果。在 CIFAR-10 数据集上,我们的方法在流行的指标(IS 和 FID)上明显优于现有技术的表现。
Abstract
generative adversarial networks (GANs) have made great progress in
synthesizing realistic images in recent years. However, they are often trained
on image datasets with either too few samples or too many classes belonging to
different data distributions. Consequently, GANs are prone to