arXiv:2512.20963v3 Announce Type: replace Abstract: Diffusion models excel at generating high-quality, diverse samples, yet they risk memorizing training data when overfit to the training objective. We analyze the distinctions between memorization and generalization in diffusion models through the lens of representation learning. By investigating a two-layer ReLU denoising autoencoder (DAE), we prove that (i) memorization corresponds to the model storing raw training samples in the learned weights for encoding and decoding, yielding localized spiky representations, whereas (ii) generalization
The rapid advancement and widespread adoption of diffusion models necessitate a deeper understanding of their underlying mechanics, especially regarding generalization versus memorization.
Understanding the mechanisms behind generalization in diffusion models is crucial for developing more robust, reliable, and ethically sound AI systems, impacting their trustworthiness and deployment.
This research provides a theoretical framework to distinguish between memorization and generalization in diffusion models, offering insights potentially leading to new architectural designs or training methodologies.
- · AI researchers
- · AI ethics and safety organizations
- · Developers of generative AI applications
- · Companies investing in foundation models
- · Developers of easily overfit generative models
- · Applications reliant on memorization for desired outcomes
Improved understanding of diffusion model behavior and limitations.
Development of new techniques to enhance generalization and prevent memorization in generative AI.
Increased public and regulatory trust in AI systems due to better explainability and controllability of their learning processes.
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Read at arXiv cs.LG