Two-Stage Score Estimation for Group-Specific Diffusion Models

Key points

• Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution with limited observations?1
• Under Besov regularity of the pooled density and a stable hierarchical composition model for the offset, we derive convergence rates that separate the costs of learning these components and identify when a simpler offset yields a faster risk-bound rate than separate fitting.1
• Under suitable boundary and regularity assumptions, we prove that the offset remains bounded and jointly regular as diffusion time approaches zero.1
• These results allow the lower time cutoff to vanish polynomially and provide total-variation guarantees for diffusion models as density estimators.1

Sources (1)

• [1]Two-Stage Score Estimation for Group-Specific Diffusion Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 14, 09:49 PM

  We develop a statistical framework for diffusion transfer that analyzes a shared pooled score together with a target-specific offset.

  Generative modeling across related distributions raises a question: how can shared structure support accurate generation for a target distribution with limited observations?

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