Understanding and Mitigating Memorization in Diffusion Models for Tabular Data

2024-12-15

arXiv.org

AI Summary

This work first systematically reveals a significant memorization phenomenon—i.e., exact replication of training samples—in diffusion models for tabular data generation, a long-overlooked issue in the tabular domain. To address it, we propose TabCutMix and TabCutMixPlus, two novel data augmentation methods: the former performs intra-class feature-segment swapping, while the latter introduces a correlation-aware clustering-guided cross-sample swapping mechanism to suppress memorization without compromising generation quality. This constitutes the first memory-mitigation framework specifically designed for tabular diffusion models, integrating rigorous theoretical analysis with practical algorithmic design. Extensive experiments across multiple real-world datasets and diverse diffusion architectures demonstrate up to 92% reduction in memorization rate, with no degradation in machine learning utility or distributional fidelity, and effective avoidance of out-of-distribution (OOD) generation.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Natural Language Processing: Generation
• Machine Learning: Mixture of Experts (MoE)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Investigating memorization phenomena in tabular diffusion models
• Analyzing factors affecting memorization in tabular data generation
• Developing data augmentation methods to mitigate memorization issues

Innovation

• Proposes TabCutMix for data augmentation
• Enhances with TabCutMixPlus using feature clustering
• Mitigates memorization while maintaining generation quality