Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models

Key points

• Our method builds on the radial-angular decomposition of multivariate heavy tails, where radius and direction become asymptotically independent.1
• We estimate the tail onset from reference and generated samples, then rescale generated extremes so that their radial survival matches the target distribution.1
• The resulting method is a lightweight post-training correction applied to a frozen diffusion or flow-matching model, requiring neither retraining nor changes to its sampling dynamics.1
• Experiments on challenging synthetic heavy-tailed distributions and real weather and wildfire datasets show that this simple correction substantially improves extreme-tail coverage using only a small number of fitting samples.1

Sources (1)

• [1]Radial Tail Repair: Post-Training Correction for Diffusion and Flow Models
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 02:24 PM

  Diffusion and flow-matching models may struggle to accurately represent distributional tails, especially for heavy-tailed data.

  Our method builds on the radial-angular decomposition of multivariate heavy tails, where radius and direction become asymptotically independent.

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