Understanding and Mitigating Memorization in Diffusion Models for Tabular Data

TL;DR

This paper investigates memorization in diffusion models for tabular data, revealing its occurrence and proposing novel augmentation techniques, TabCutMix and TabCutMixPlus, to mitigate memorization and improve data generation quality.

Contribution

It provides the first comprehensive analysis of memorization in tabular diffusion models and introduces effective augmentation methods to address this issue.

Findings

Memorization increases with training epochs in tabular diffusion models.

TabCutMix and TabCutMixPlus effectively reduce memorization.

Proposed methods maintain high-quality data generation.

Abstract

Tabular data generation has attracted significant research interest in recent years, with the tabular diffusion models greatly improving the quality of synthetic data. However, while memorization, where models inadvertently replicate exact or near-identical training data, has been thoroughly investigated in image and text generation, its effects on tabular data remain largely unexplored. In this paper, we conduct the first comprehensive investigation of memorization phenomena in diffusion models for tabular data. Our empirical analysis reveals that memorization appears in tabular diffusion models and increases with larger training epochs. We further examine the influence of factors such as dataset sizes, feature dimensions, and different diffusion models on memorization. Additionally, we provide a theoretical explanation for why memorization occurs in tabular diffusion models. To address this issue, we propose TabCutMix, a simple yet effective data augmentation technique that exchanges randomly selected feature segments between random same-class training sample pairs. Building upon this, we introduce TabCutMixPlus, an enhanced method that clusters features based on feature correlations and ensures that features within the same cluster are exchanged together during augmentation. This clustering mechanism mitigates out-of-distribution (OOD) generation issues by maintaining feature coherence. Experimental results across various datasets and diffusion models demonstrate that TabCutMix effectively mitigates memorization while maintaining high-quality data generation.