Though generative adversarial networks (GANs) areprominent models to generate
realistic and crisp images,they often encounter the mode collapse problems and
arehard to train, which comes from approximating the intrinsicdiscontinuous
distribution transform map with continuousDNNs. The recently proposed AE-OT
model addresses thisproblem by explicitly computing the discontinuous
distribu-tion transform map through solving a semi-discrete optimaltransport
(OT) map in the latent space of the autoencoder.However the generated images
are blurry. In this paper, wepropose the AE-OT-GAN model to utilize the
advantages ofthe both models: generate high quality images and at thesame time
overcome the mode collapse/mixture problems.Specifically, we first faithfully
embed the low dimensionalimage manifold into the latent space by training an
autoen-coder (AE). Then we compute the optimal transport (OT)map that pushes
forward the uniform distribution to the la-tent distribution supported on the
latent manifold. Finally,our GAN model is trained to generate high quality
imagesfrom the latent distribution, the distribution transform mapfrom which to
the empirical data distribution will be con-tinuous. The paired data between
the latent code and thereal images gives us further constriction about the
generator.Experiments on simple MNIST dataset and complex datasetslike Cifar-10
and CelebA show the efficacy and efficiency ofour proposed method.

本文提出 AE-OT-GAN 模型，借鉴 AE-OT 模型解决了 GAN 模型生成的图片模式坍塌 / 混合问题，同时生成高质量图像；最终通过潜在分布转换映射生成连续的数据分布。实验证明本文方法在简单的 MNIST 数据集和复杂的数据集（如 Cifar-10 和 CelebA）中效果良好。