Summary of Amortized Posterior Sampling with Diffusion Prior Distillation, by Abbas Mammadov et al.
Amortized Posterior Sampling with Diffusion Prior Distillation
by Abbas Mammadov, Hyungjin Chung, Jong Chul Ye
First submitted to arxiv on: 25 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel variational inference approach is introduced to efficiently sample from the posterior distribution for solving inverse problems. The proposed method builds upon a pre-trained diffusion model and trains a conditional flow model to minimize the divergence between the proposal variational distribution and the implicitly defined posterior distribution through the diffusion model. This enables amortized sampling from the posterior distribution with a single neural flow evaluation (NFE). The method paves the way for distilling a diffusion prior for efficient posterior sampling. The approach is demonstrated to be applicable to standard signals in Euclidean space, as well as signals on manifolds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’re proposing a new way to solve tricky problems by generating samples from a probability distribution. It’s based on a special kind of machine learning model that helps us find the right answer. This method can be used for many types of problems and is really good at solving them quickly and accurately. It even works with complicated data that doesn’t fit into simple spaces. |
Keywords
* Artificial intelligence * Diffusion * Diffusion model * Inference * Machine learning * Probability
