General Invertible Transformations for Flow-Based Generative Modeling
Abstract
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.
Cite
Text
Tomczak. "General Invertible Transformations for Flow-Based Generative Modeling." ICML 2021 Workshops: INNF, 2021.Markdown
[Tomczak. "General Invertible Transformations for Flow-Based Generative Modeling." ICML 2021 Workshops: INNF, 2021.](https://mlanthology.org/icmlw/2021/tomczak2021icmlw-general/)BibTeX
@inproceedings{tomczak2021icmlw-general,
title = {{General Invertible Transformations for Flow-Based Generative Modeling}},
author = {Tomczak, Jakub Mikolaj},
booktitle = {ICML 2021 Workshops: INNF},
year = {2021},
url = {https://mlanthology.org/icmlw/2021/tomczak2021icmlw-general/}
}