  Discrete flow-based models are a recently proposed class of generative models
that learn invertible transformations for discrete random variables. Since they
do not require data dequantization and maximize an exact likelihood objective,
they can be used in a straight-forward manner for lossless compression. In this
paper, we introduce a new discrete flow-based model for categorical random
variables: Discrete Denoising Flows (DDFs). In contrast with other discrete
flow-based models, our model can be locally trained without introducing
gradient bias. We show that DDFs outperform Discrete Flows on modeling a toy
example, binary MNIST and Cityscapes segmentation maps, measured in
log-likelihood.
