# FCT-GAN: Enhancing Table Synthesis via Fourier Transform

arXiv: https://arxiv.org/abs/2210.06239 · HTML: https://mlsanity.com/paper/2210.06239/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 25 references that are in the map.
- First posted on arXiv 12 Oct 2022.

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

- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [Fourier Neural Operator for Parametric Partial Differential Equations](https://mlsanity.com/paper/2010.08895/) (2020)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [FNet: Mixing Tokens with Fourier Transforms](https://mlsanity.com/paper/2105.03824/) (2021)
- [Global Filter Networks for Image Classification](https://mlsanity.com/paper/2107.00645/) (2021)
- [Generating Multi-label Discrete Patient Records using Generative Adversarial Networks](https://mlsanity.com/paper/1703.06490/) (2017)
- [Data Synthesis based on Generative Adversarial Networks](https://mlsanity.com/paper/1806.03384/) (2018)
- [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015)
- [CTAB-GAN: Effective Table Data Synthesizing](https://mlsanity.com/paper/2102.08369/) (2021)
- [Fast Training of Convolutional Networks through FFTs](https://mlsanity.com/paper/1312.5851/) (2013)
- [CTAB-GAN+: Enhancing Tabular Data Synthesis](https://mlsanity.com/paper/2204.00401/) (2022)
- [Invertible Tabular GANs: Killing Two Birds with OneStone for Tabular Data Synthesis](https://mlsanity.com/paper/2202.03636/) (2022)
- [Generating Synthetic but Plausible Healthcare Record Datasets](https://mlsanity.com/paper/1807.01514/) (2018)

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

- [TabuLa: Harnessing Language Models for Tabular Data Synthesis](https://mlsanity.com/paper/2310.12746/) (2023)
- [A Comprehensive Survey of Synthetic Tabular Data Generation](https://mlsanity.com/paper/2504.16506/) (2025)
- [Improve Fidelity and Utility of Synthetic Credit Card Transaction Time Series from Data-centric Perspective](https://mlsanity.com/paper/2401.00965/) (2024)
- [zGAN: An Outlier-focused Generative Adversarial Network For Realistic Synthetic Data Generation](https://mlsanity.com/paper/2410.20808/) (2024)
- [TransCORALNet: A Two-Stream Transformer CORAL Networks for Supply Chain Credit Assessment Cold Start](https://mlsanity.com/paper/2311.18749/) (2023)

## Abstract (arXiv)

Synthetic tabular data emerges as an alternative for sharing knowledge while adhering to restrictive data access regulations, e.g., European General Data Protection Regulation (GDPR). Mainstream state-of-the-art tabular data synthesizers draw methodologies from Generative Adversarial Networks (GANs), which are composed of a generator and a discriminator. While convolution neural networks are shown to be a better architecture than fully connected networks for tabular data synthesizing, two key properties of tabular data are overlooked: (i) the global correlation across columns, and (ii) invariant synthesizing to column permutations of input data. To address the above problems, we propose a Fourier conditional tabular generative adversarial network (FCT-GAN). We introduce feature tokenization and Fourier networks to construct a transformer-style generator and discriminator, and capture both local and global dependencies across columns. The tokenizer captures local spatial features and transforms original data into tokens. Fourier networks transform tokens to frequency domains and element-wisely multiply a learnable filter. Extensive evaluation on benchmarks and real-world data shows that FCT-GAN can synthesize tabular data with high machine learning utility (up to 27.8% better than state-of-the-art baselines) and high statistical similarity to the original data (up to 26.5% better), while maintaining the global correlation across columns, especially on high dimensional dataset.