# HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation

arXiv: https://arxiv.org/abs/2111.07015 · HTML: https://mlsanity.com/paper/2111.07015/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 48 references that are in the map.
- First posted on arXiv 13 Nov 2021.

## Builds on (most cited first, 17 of 17)

- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Certified Robustness to Adversarial Examples with Differential Privacy](https://mlsanity.com/paper/1802.03471/) (2018)
- [Training Deep Networks with Synthetic Data: Bridging the Reality Gap by Domain Randomization](https://mlsanity.com/paper/1804.06516/) (2018)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning](https://mlsanity.com/paper/1705.07761/) (2017)
- [MedGAN: Medical Image Translation using GANs](https://mlsanity.com/paper/1806.06397/) (2018)
- [Distributed Differential Privacy via Shuffling](https://mlsanity.com/paper/1808.01394/) (2018)
- [MD-GAN: Multi-Discriminator Generative Adversarial Networks for Distributed Datasets](https://mlsanity.com/paper/1811.03850/) (2018)
- [DP-ADMM: ADMM-based Distributed Learning with Differential Privacy](https://mlsanity.com/paper/1808.10101/) (2018)
- [Local Differential Privacy and Its Applications: A Comprehensive Survey](https://mlsanity.com/paper/2008.03686/) (2020)
- [PPGAN: Privacy-preserving Generative Adversarial Network](https://mlsanity.com/paper/1910.02007/) (2019)
- [Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining](https://mlsanity.com/paper/1806.06151/) (2018)
- [Answering Range Queries Under Local Differential Privacy](https://mlsanity.com/paper/1812.10942/) (2018)
- [Technical Privacy Metrics: a Systematic Survey](https://mlsanity.com/paper/1512.00327/) (2015)
- [Differential Privacy for Eye-Tracking Data](https://mlsanity.com/paper/1904.06809/) (2019)
- [Element Level Differential Privacy: The Right Granularity of Privacy](https://mlsanity.com/paper/1912.04042/) (2019)
- [Differential Privacy of Aggregated DC Optimal Power Flow Data](https://mlsanity.com/paper/1903.11237/) (2019)

## Abstract (arXiv)

Synthetic data generation overcomes limitations of real-world machine learning. Traditional methods are valuable for augmenting costly datasets but only optimize one criterion: realism. In this paper, we tackle the problem of generating synthetic data that optimize multiple criteria. This goal is necessary when real data are replaced by synthetic for privacy preservation. We introduce HydraGAN, a new approach to synthetic data generation that introduces multiple generator and discriminator agents into the system. The multi-agent GAN optimizes the goal of privacy-preservation as well as data realism. To facilitate multi-agent training, we adapt game-theoretic principles to offer equilibrium guarantees. We observe that HydraGAN outperforms baseline methods for three datasets for multiple criteria of maximizing data realism, maximizing model accuracy, and minimizing re-identification risk.