Density estimation using Real NVP

@article{Dinh2017DensityEU,
  title={Density estimation using Real NVP},
  author={Laurent Dinh and Jascha Sohl-Dickstein and Samy Bengio},
  journal={ArXiv},
  year={2017},
  volume={abs/1605.08803}
}
Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful, stably invertible, and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact and… CONTINUE READING

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