General Invertible Transformations for Flow-based Generative Modeling

In this paper, we present a new class of invertible transformations. We indicate that many well-known invertible tranformations 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 preliminary experiments on toy 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.