  In this work, we investigate the use of normalizing flows to model
conditional distributions. In particular, we use our proposed method to analyze
inverse problems with invertible neural networks by maximizing the posterior
likelihood. Our method uses only a single loss and is easy to train. This is an
improvement on the previous method that solves similar inverse problems with
invertible neural networks but which involves a combination of several loss
terms with ad-hoc weighting. In addition, our method provides a natural
framework to incorporate conditioning in normalizing flows, and therefore, we
can train an invertible network to perform conditional generation. We analyze
our method and perform a careful comparison with previous approaches. Simple
experiments show the effectiveness of our method, and more comprehensive
experimental evaluations are undergoing.
