论文标题
往返：深层生成神经密度估计器
Roundtrip: A Deep Generative Neural Density Estimator
论文作者
论文摘要
密度估计是统计和机器学习中的一个基本问题。在这项研究中，我们提出了基于深层生成模型的通用神经密度估计量。往返保留生成对抗网络（GAN）的生成力量，但也提供了密度值的估计值。与以前的神经密度估计器不同，从潜在空间到数据空间的转换为严格的条件，往返可以使用更通用的映射。在一系列实验中，往返在各种密度估计任务范围内实现最先进的性能。
Density estimation is a fundamental problem in both statistics and machine learning. In this study, we proposed Roundtrip as a general-purpose neural density estimator based on deep generative models. Roundtrip retains the generative power of generative adversarial networks (GANs) but also provides estimates of density values. Unlike previous neural density estimators that put stringent conditions on the transformation from the latent space to the data space, Roundtrip enables the use of much more general mappings. In a series of experiments, Roundtrip achieves state-of-the-art performance in a diverse range of density estimation tasks.
