  Normalizing flows are bijective mappings between inputs and latent
representations with a fully factorized distribution. They are very attractive
due to exact likelihood valuation and efficient sampling. However, their
effective capacity is often insufficient since the bijectivity constraint
limits the model width. We address this issue by incrementally padding
intermediate representations with noise. We precondition the noise in
accordance with previous invertible units, which we describe as cross-unit
coupling. Our invertible glow-like modules increase the model expressivity by
fusing a densely connected block with Nystrom self-attention. We refer to our
architecture as DenseFlow since both cross-unit and intra-module couplings rely
on dense connectivity. Experiments show significant improvements due to the
proposed contributions and reveal state-of-the-art density estimation under
moderate computing budgets.
