Sobolev Regularized Score Difference Estimation in Diffusion Models
Estimating the difference of two Stein's score functions is a fundamental problem in generative modeling. In particular, score differences arise naturally in transfer learning, where the score difference provides the mechanism for adapting a pre-trained model to a new target distribution, and in diffusion model-based post-training methods such as discriminator guidance. Existing estimators for score differences in these settings either lack of statistical consistency or are difficult to scale up in high-dimensions. We propose a statistically consistent and scalable estimator for score differences based on Sobolev regularization, which plays a crucial role in ensuring consistency and stabl...
| Authors | Chenghan Xie, Jose Blanchet, Renyuan Xu |
|---|---|
| Primary category | stat.ML |
| Published | 18 August 2026 |
| Last updated at source | 24 August 2026 |
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