Roundtrip: A Deep Generative Neural Density Estimator
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Density estimation is a fundamental problem in both statistics and machine learning. We proposed Roundtrip as a universal neural density estimator based on deep generative models. Roundtrip exploits the advantage of GANs for generating samples and estimates density by either importance sampling or Laplace approximation. Unlike prior studies modeling target density by constructing a tractable Jacobian w.r.t to a base density (e.g., Gaussian), Roundtrip learns target density by generating a manifold from a base density to approximate the distribution of observation data. In a series of experiments, Roundtrip achieves state-of-the-art performance in a diverse range of density estimation tasks.
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