# JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets

arXiv: https://arxiv.org/abs/1806.02978 · HTML: https://mlsanity.com/paper/1806.02978/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jun 2018.

## Built on by (most cited first, 7 of 7)

- [Free Lunch for Domain Adversarial Training: Environment Label Smoothing](https://mlsanity.com/paper/2302.00194/) (2023)
- [Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions](https://mlsanity.com/paper/2211.00568/) (2022)
- [Synergy Between Sufficient Changes and Sparse Mixing Procedure for Disentangled Representation Learning](https://mlsanity.com/paper/2503.00639/) (2025)
- [FRIDA -- Generative Feature Replay for Incremental Domain Adaptation](https://mlsanity.com/paper/2112.14316/) (2021)
- [Preserving Domain Private Representation via Mutual Information Maximization](https://mlsanity.com/paper/2201.03102/) (2022)
- [Continuous-Time Video Generation via Learning Motion Dynamics with Neural ODE](https://mlsanity.com/paper/2112.10960/) (2021)
- [Distribution estimation and change-point estimation for time series via DNN-based GANs](https://mlsanity.com/paper/2211.14577/) (2022)

## Abstract (arXiv)

A new generative adversarial network is developed for joint distribution matching. Distinct from most existing approaches, that only learn conditional distributions, the proposed model aims to learn a joint distribution of multiple random variables (domains). This is achieved by learning to sample from conditional distributions between the domains, while simultaneously learning to sample from the marginals of each individual domain. The proposed framework consists of multiple generators and a single softmax-based critic, all jointly trained via adversarial learning. From a simple noise source, the proposed framework allows synthesis of draws from the marginals, conditional draws given observations from a subset of random variables, or complete draws from the full joint distribution. Most examples considered are for joint analysis of two domains, with examples for three domains also presented.