  Despite substantial progress in signal source separation, results for richly
structured data continue to contain perceptible artifacts. In contrast, recent
deep generative models can produce authentic samples in a variety of domains
that are indistinguishable from samples of the data distribution. This paper
introduces a Bayesian approach to source separation that uses generative models
as priors over the components of a mixture of sources, and noise-annealed
Langevin dynamics to sample from the posterior distribution of sources given a
mixture. This decouples the source separation problem from generative modeling,
enabling us to directly use cutting-edge generative models as priors. The
method achieves state-of-the-art performance for MNIST digit separation. We
introduce new methodology for evaluating separation quality on richer datasets,
providing quantitative evaluation of separation results on CIFAR-10. We also
provide qualitative results on LSUN.

Machine Learning;Machine Learning