# GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture Modeling

arXiv: https://arxiv.org/abs/2003.06788 · HTML: https://mlsanity.com/paper/2003.06788/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2020.

## Built on by (most cited first, 11 of 11)

- [Neural Optimal Transport](https://mlsanity.com/paper/2201.12220/) (2022)
- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Diff-Font: Diffusion Model for Robust One-Shot Font Generation](https://mlsanity.com/paper/2212.05895/) (2022)
- [ISF-GAN: An Implicit Style Function for High-Resolution Image-to-Image Translation](https://mlsanity.com/paper/2109.12492/) (2021)
- [Free on the Fly: Enhancing Flexibility in Test-Time Adaptation with Online EM](https://mlsanity.com/paper/2507.06973/) (2025)
- [A Survey of Multimodal Composite Editing and Retrieval](https://mlsanity.com/paper/2409.05405/) (2024)
- [Domain Translation via Latent Space Mapping](https://mlsanity.com/paper/2212.03361/) (2022)
- [Domain-Scalable Unpaired Image Translation via Latent Space Anchoring](https://mlsanity.com/paper/2306.14879/) (2023)
- [Bridging the Gap between Label- and Reference-based Synthesis in Multi-attribute Image-to-Image Translation](https://mlsanity.com/paper/2110.05055/) (2021)
- [Variational Bayesian Framework for Advanced Image Generation with Domain-Related Variables](https://mlsanity.com/paper/2305.13872/) (2023)
- [SDA-GAN: Unsupervised Image Translation Using Spectral Domain Attention-Guided Generative Adversarial Network](https://mlsanity.com/paper/2110.02873/) (2021)

## Abstract (arXiv)

Unsupervised image-to-image translation (UNIT) aims at learning a mapping between several visual domains by using unpaired training images. Recent studies have shown remarkable success for multiple domains but they suffer from two main limitations: they are either built from several two-domain mappings that are required to be learned independently, or they generate low-diversity results, a problem known as mode collapse. To overcome these limitations, we propose a method named GMM-UNIT, which is based on a content-attribute disentangled representation where the attribute space is fitted with a GMM. Each GMM component represents a domain, and this simple assumption has two prominent advantages. First, it can be easily extended to most multi-domain and multi-modal image-to-image translation tasks. Second, the continuous domain encoding allows for interpolation between domains and for extrapolation to unseen domains and translations. Additionally, we show how GMM-UNIT can be constrained down to different methods in the literature, meaning that GMM-UNIT is a unifying framework for unsupervised image-to-image translation.