تقرير
Understanding DDPM Latent Codes Through Optimal Transport
| العنوان: | Understanding DDPM Latent Codes Through Optimal Transport |
|---|---|
| المؤلفون: | Khrulkov, Valentin, Ryzhakov, Gleb, Chertkov, Andrei, Oseledets, Ivan |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science Mathematics Statistics |
| مصطلحات موضوعية: | Statistics - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Mathematics - Analysis of PDEs, Mathematics - Numerical Analysis |
| الوصف: | Diffusion models have recently outperformed alternative approaches to model the distribution of natural images, such as GANs. Such diffusion models allow for deterministic sampling via the probability flow ODE, giving rise to a latent space and an encoder map. While having important practical applications, such as estimation of the likelihood, the theoretical properties of this map are not yet fully understood. In the present work, we partially address this question for the popular case of the VP SDE (DDPM) approach. We show that, perhaps surprisingly, the DDPM encoder map coincides with the optimal transport map for common distributions; we support this claim theoretically and by extensive numerical experiments. |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2202.07477 |
| رقم الأكسشن: | edsarx.2202.07477 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |