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About this paper
Taehwan Lee, Kyeongkook Seo, Jaejun Yoo, Sung Whan Yoon
The paper investigates the role of loss surface flatness in diffusion models, demonstrating a theoretical connection between flatter minima and robustness to target prior distribution perturbations. Empirical evidence indicates that promoting flatness enhances generative performance, mitigates exposure bias, and improves resilience to model quantization.
Key insights
- A theoretical connection exists between parameter perturbations in generative models and shifts in the target prior distribution, implying that flatter minima lead to greater robustness against these distributional changes.
- Sharpness-Aware Minimization (SAM) is an effective method for explicitly inducing flatter minima in diffusion models, although it requires stronger regularization strengths compared to discriminative models.
- Ensemble-like averaging methods (EMA, SWA) provide regularization benefits and improve performance but do not significantly induce additional flatness in the loss landscape of diffusion models.
Summary by alphaXiv.
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