In this paper, we present a novel and principled approach to learn the optimal transport
between two distributions, from samples. Guided by the optimal transport theory, we learn …

This paper studies the universal approximation property of deep neural networks for
representing probability distributions. Given a target distribution $\pi $ and a source …

This work introduces an optimal transportation (OT) view of generative adversarial networks
(GANs). Natural datasets have intrinsic patterns, which can be summarized as the manifold …

首先介绍了生成对抗网络基本理论, 应用场景和研究现状, 并列举了其亟待改进的问题.
围绕针对提升模型训练效率, 提升生成样本质量和降低模式崩溃现象发生可能性3 类问题的解决 …

Deep learning has achieved significant success on intelligent medical treatments, such as
automatic diagnosis and analysis of medical data. To train an automatic diagnosis system …

Data usually resides on a manifold, and the minimal dimension of such a manifold is called
its intrinsic dimension. This fundamental data property is not considered in the generative …

Despite the significant progress that has been made in the training of Generative Adversarial
Networks (GANs), the mode collapse problem remains a major challenge in training GANs …

Point cloud generative models have aroused increasing concern for their realistic
generation potentialities. However, most existing methods adopt deep-neural-network …

Mode collapse has been a persisting challenge in generative adversarial networks (GANs),
and it directly affects the applications of GAN in many domains. Existing works that attempt to …

This paper studies the estimation of large-scale optimal transport maps (OTM), which is a
well known challenging problem owing to the curse of dimensionality. Existing literature …