  Normalizing flows (NF) use a continuous generator to map a simple latent
(e.g. Gaussian) distribution, towards an empirical target distribution
associated with a training data set. Once trained by minimizing a variational
objective, the learnt map provides an approximate generative model of the
target distribution. Since standard NF implement differentiable maps, they may
suffer from pathological behaviors when targeting complex distributions. For
instance, such problems may appear for distributions on multi-component
topologies or characterized by multiple modes with high probability regions
separated by very unlikely areas. A typical symptom is the explosion of the
Jacobian norm of the transformation in very low probability areas. This paper
proposes to overcome this issue thanks to a new Markov chain Monte Carlo
algorithm to sample from the target distribution in the latent domain before
transporting it back to the target domain. The approach relies on a Metropolis
adjusted Langevin algorithm (MALA) whose dynamics explicitly exploits the
Jacobian of the transformation. Contrary to alternative approaches, the
proposed strategy preserves the tractability of the likelihood and it does not
require a specific training. Notably, it can be straightforwardly used with any
pre-trained NF network, regardless of the architecture. Experiments conducted
on synthetic and high-dimensional real data sets illustrate the efficiency of
the method.
