# General Invertible Transformations for Flow-based Generative Modeling

arXiv: https://arxiv.org/abs/2011.15056 · HTML: https://mlsanity.com/paper/2011.15056/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2020.

## Built on by (most cited first, 4 of 4)

- [Cycle-free CycleGAN using Invertible Generator for Unsupervised Low-Dose CT Denoising](https://mlsanity.com/paper/2104.08538/) (2021)
- [Discrete Denoising Flows](https://mlsanity.com/paper/2107.11625/) (2021)
- [Mixed Variational Flows for Discrete Variables](https://mlsanity.com/paper/2308.15613/) (2023)
- [Optimal Transport Driven Asymmetric Image-to-Image Translation for Nuclei Segmentation of Histological Images](https://mlsanity.com/paper/2506.07023/) (2025)

## Abstract (arXiv)

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.