  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.
