  In this paper, we present a new class of invertible transformations with an
application to flow-based generative models. We indicate that many well-known
invertible transformations in reversible logic and reversible neural networks
could be derived from our proposition. Next, we propose two new coupling layers
that are important building blocks of flow-based generative models. In the
experiments on digit data, we present how these new coupling layers could be
used in Integer Discrete Flows (IDF), and that they achieve better results than
standard coupling layers used in IDF and RealNVP.
