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Roundtrip: A Deep Generative Neural Density Estimator

2020-04-20Code Available1· sign in to hype

Qiao Liu, Jiaze Xu, Rui Jiang, Wing Hung Wong

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Abstract

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.

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