Summary of Understanding and Mitigating Memorization in Diffusion Models For Tabular Data, by Zhengyu Fang et al.
Understanding and Mitigating Memorization in Diffusion Models for Tabular Data
by Zhengyu Fang, Zhimeng Jiang, Huiyuan Chen, Xiao Li, Jing Li
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper investigates memorization in tabular diffusion models, a phenomenon where models replicate exact or near-identical training data. The authors reveal that memorization increases with larger training epochs and is influenced by factors like dataset sizes, feature dimensions, and different diffusion models. To address this issue, the authors propose two techniques: TabCutMix, which exchanges randomly selected feature segments between same-class training sample pairs, and TabCutMixPlus, an enhanced method that clusters features based on correlations to ensure feature coherence during augmentation. Experimental results demonstrate that these techniques effectively mitigate memorization while maintaining high-quality data generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well machine learning models can copy or remember their training data when generating new tables. They find that this “memorization” happens more often with larger training sets and longer model training times. To fix this problem, the authors suggest two new ways to mix up the training data: TabCutMix and TabCutMixPlus. These methods help keep the generated data looking natural by swapping feature patterns between similar examples. The results show that these techniques can improve table generation quality while reducing memorization. |
Keywords
» Artificial intelligence » Diffusion » Machine learning
