  Normalizing flows (NF) recently gained attention as a way to construct
generative networks with exact likelihood calculation out of composable layers.
However, NF is restricted to dimension-preserving transformations. Surjection
VAE (SurVAE) has been proposed to extend NF to dimension-altering
transformations. Such networks are desirable because they are expressive and
can be precisely trained. We show that the approaches are a re-invention of PDF
projection, which appeared over twenty years earlier and is much further
developed.
