# Tab2Visual: Overcoming Limited Data in Tabular Data Classification Using Deep Learning with Visual Representations

arXiv: https://arxiv.org/abs/2502.07181 · HTML: https://mlsanity.com/paper/2502.07181/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2025.

## Built on by (most cited first, 3 of 3)

- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models](https://mlsanity.com/paper/2601.04110/) (2026)
- [TabSOM: A tabular-to-image encoding method based on self-organizing maps](https://mlsanity.com/paper/2608.13513/) (2026)

## Abstract (arXiv)

This research addresses the challenge of limited data in tabular data classification, particularly prevalent in domains with constraints like healthcare. We propose Tab2Visual, a novel approach that transforms heterogeneous tabular data into visual representations, enabling the application of powerful deep learning models. Tab2Visual effectively addresses data scarcity by incorporating novel image augmentation techniques and facilitating transfer learning. We extensively evaluate the proposed approach on diverse tabular datasets, comparing its performance against a wide range of machine learning algorithms, including classical methods, tree-based ensembles, and state-of-the-art deep learning models specifically designed for tabular data. We also perform an in-depth analysis of factors influencing Tab2Visual's performance. Our experimental results demonstrate that Tab2Visual outperforms other methods in classification problems with limited tabular data.