All the normalizing flows-based papers I read (NICE, RealNVP, Glow, etc.) show experiments on high dimensional image datasets. I am looking for works that analyze the capacity of NFs to learn simple 1/2d distributions. I am aware of the 2d experiments in [Rezende and Mohamed, 2015] but, as far as I understand, for the 2d datasets they train by directly minimizing KL (and do not train using samples) because the analytic inverse of Planar flow does not exist.