# Correlated discrete data generation using adversarial training

arXiv: https://arxiv.org/abs/1804.00925 · HTML: https://mlsanity.com/paper/1804.00925/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 5 of 5)

- [STaSy: Score-based Tabular data Synthesis](https://mlsanity.com/paper/2210.04018/) (2022)
- [OCT-GAN: Neural ODE-based Conditional Tabular GANs](https://mlsanity.com/paper/2105.14969/) (2021)
- [Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis](https://mlsanity.com/paper/2202.03636/) (2022)
- [On the Inherent Privacy Properties of Discrete Denoising Diffusion Models](https://mlsanity.com/paper/2310.15524/) (2023)
- [FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance](https://mlsanity.com/paper/2201.11486/) (2022)

## Abstract (arXiv)

Generative Adversarial Networks (GAN) have shown great promise in tasks like synthetic image generation, image inpainting, style transfer, and anomaly detection. However, generating discrete data is a challenge. This work presents an adversarial training based correlated discrete data (CDD) generation model. It also details an approach for conditional CDD generation. The results of our approach are presented over two datasets; job-seeking candidates skill set (private dataset) and MNIST (public dataset). From quantitative and qualitative analysis of these results, we show that our model performs better as it leverages inherent correlation in the data, than an existing model that overlooks correlation.