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arXiv · 2503.06444

Towards Synthesizing High-Dimensional Tabular Data with Limited Samples

Zuqing Li·Junhao Gan·Jianzhong Qi
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Abstract

Diffusion-based tabular data synthesis models have yielded promising results. However, when the data dimensionality increases, existing models tend to degenerate and may perform even worse than simpler, non-diffusion-based models. This is because limited training samples in high-dimensional space often hinder generative models from capturing the distribution accurately. To mitigate the insufficient learning signals and to stabilize training under such conditions, we propose CtrTab, a condition-controlled diffusion model that injects perturbed ground-truth samples as auxiliary inputs during training. This design introduces an implicit L2 regularization on the model's sensitivity to the control signal, improving robustness and stability in high-dimensional, low-data scenarios. Experimental results across multiple datasets show that CtrTab outperforms state-of-the-art models, with a performance gap in accuracy over 90% on average.

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Zuqing Li, Junhao Gan, Jianzhong Qi. 2025-03-09. Towards Synthesizing High-Dimensional Tabular Data with Limited Samples. https://arxiv.org/abs/2503.06444

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