  Deep generative models complement Markov-chain-Monte-Carlo methods for
efficiently sampling from high-dimensional distributions. Among these methods,
explicit generators, such as Normalising Flows (NFs), in combination with the
Metropolis Hastings algorithm have been extensively applied to get unbiased
samples from target distributions. We systematically study central problems in
conditional NFs, such as high variance, mode collapse and data efficiency. We
propose adversarial training for NFs to ameliorate these problems. Experiments
are conducted with low-dimensional synthetic datasets and XY spin models in two
spatial dimensions.
