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Provable Separations between Memorization and Generalization in Diffusion Models

arXiv — stat.ML•Thursday, November 6, 2025 at 5:00:00 AM
PositiveArtificial Intelligence

Provable Separations between Memorization and Generalization in Diffusion Models

Recent research on diffusion models highlights a significant breakthrough in understanding the balance between memorization and generalization. While these models have shown impressive results, they often struggle with reproducing training data instead of creating new content. This study not only sheds light on the theoretical aspects of memorization but also emphasizes the importance of addressing privacy and safety concerns. By developing a dual-separation approach, the findings could pave the way for more innovative and secure applications of diffusion models, making this research crucial for the future of AI.
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