# Generating Multi-Categorical Samples with Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1807.01202 · HTML: https://mlsanity.com/paper/1807.01202/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jul 2018.

## Built on by (most cited first, 17 of 17)

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- [Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV](https://mlsanity.com/paper/2208.08655/) (2022)
- [Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models](https://mlsanity.com/paper/2303.12281/) (2023)
- [Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study](https://mlsanity.com/paper/2112.01925/) (2021)
- [Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata](https://mlsanity.com/paper/2207.03339/) (2022)
- [Bi-Discriminator Class-Conditional Tabular GAN](https://mlsanity.com/paper/2111.06549/) (2021)
- [Generative Modeling of Complex Data](https://mlsanity.com/paper/2202.02145/) (2022)
- [Privacy-Utility Trades in Crowdsourced Signal Map Obfuscation](https://mlsanity.com/paper/2201.04782/) (2022)
- [A Unified Framework for Tabular Generative Modeling: Loss Functions, Benchmarks, and Improved Multi-objective Bayesian Optimization Approaches](https://mlsanity.com/paper/2405.16971/) (2024)
- [FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance](https://mlsanity.com/paper/2201.11486/) (2022)
- [Assessing Generative Models for Structured Data](https://mlsanity.com/paper/2503.20903/) (2025)
- [Joint Distributional Learning via Cramer-Wold Distance](https://mlsanity.com/paper/2310.16374/) (2023)

## Abstract (arXiv)

We propose a method to train generative adversarial networks on mutivariate feature vectors representing multiple categorical values. In contrast to the continuous domain, where GAN-based methods have delivered considerable results, GANs struggle to perform equally well on discrete data. We propose and compare several architectures based on multiple (Gumbel) softmax output layers taking into account the structure of the data. We evaluate the performance of our architecture on datasets with different sparsity, number of features, ranges of categorical values, and dependencies among the features. Our proposed architecture and method outperforms existing models.