✍️ Text Generation

🧪 ICML2025 · 1 paper notes

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Understanding and Mitigating Memorization in Diffusion Models for Tabular Data

This work presents the first systematic study of the memorization phenomenon in tabular diffusion models, finding that memorization intensifies as training epochs increase and is strongly correlated with dataset size. It proposes TabCutMix/TabCutMixPlus to mitigate memorization via feature-segment swapping while maintaining generation quality.