  Density deconvolution is the task of estimating a probability density
function given only noise-corrupted samples. We can fit a Gaussian mixture
model to the underlying density by maximum likelihood if the noise is normally
distributed, but would like to exploit the superior density estimation
performance of normalizing flows and allow for arbitrary noise distributions.
Since both adjustments lead to an intractable likelihood, we resort to
amortized variational inference. We demonstrate some problems involved in this
approach, however, experiments on real data demonstrate that flows can already
out-perform Gaussian mixtures for density deconvolution.
