Generative Adversarial Networks have surprising ability for generating sharp
and realistic images, though they are known to suffer from the so-called mode
collapse problem. In this paper, we propose a new GAN variant called Mixture
Density GAN that while being capable of generating high-quality images,
overcomes this problem by encouraging the Discriminator to form clusters in its
embedding space, which in turn leads the Generator to exploit these and
discover different modes in the data. This is achieved by positioning Gaussian
density functions in the corners of a simplex, using the resulting Gaussian
mixture as a likelihood function over discriminator embeddings, and formulating
an objective function for GAN training that is based on these likelihoods. We
demonstrate empirically (1) the quality of the generated images in Mixture
Density GAN and their strong similarity to real images, as measured by the
Fr\'echet Inception Distance (FID), which compares very favourably with
state-of-the-art methods, and (2) the ability to avoid mode collapse and
discover all data modes.

本文提出了一种新型 GAN 变体 Mixutre Density GAN，通过在判别器嵌入空间中形成聚类来打破生成器的模式崩塌问题，从而发现不同的数据模式，并在生成高质量图像方面表现出色。