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Generative adversarial networks (GANs) have achieved rapid progress inlearning rich data distributions. However, we argue about two main issues inexisting techniques. First, the low quality problem where the learneddistribution has massive low quality samples. Second, the missing modes problemwhere the learned distribution misses some certain regions of the real datadistribution. To address these two issues, we propose a novel prior thatcaptures the whole real data distribution for GANs, which are called PriorGANs.To be specific, we adopt a simple yet elegant Gaussian Mixture Model (GMM) tobuild an explicit probability distribution on the feature level for the wholereal data. By maximizing the probability of generated data, we can push the lowquality samples to high quality. Meanwhile, equipped with the prior, we canestimate the missing modes in the learned distribution and design a samplingstrategy on the real data to solve the problem. The proposed real data priorcan generalize to various training settings of GANs, such as LSGAN, WGAN-GP,SNGAN, and even the StyleGAN. Our experiments demonstrate that PriorGANsoutperform the state-of-the-art on the CIFAR-10, FFHQ, LSUN-cat, and LSUN-birddatasets by large margins.

PriorGAN: Real Data Prior for Generative Adversarial Nets