Summary of Posterior Sampling Via Langevin Dynamics Based on Generative Priors, by Vishal Purohit et al.
Posterior sampling via Langevin dynamics based on generative priors
by Vishal Purohit, Matthew Repasky, Jianfeng Lu, Qiang Qiu, Yao Xie, Xiuyuan Cheng
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); 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 The proposed method for efficient posterior sampling in high-dimensional spaces using generative models offers significant promise for various applications, including inverse problems and guided generation tasks. By simulating Langevin dynamics in the noise space of a pre-trained generative model, this approach enables seamless exploration of the posterior without restarting the entire generative process for each new sample, drastically reducing computational overhead. The method is theoretically proven to approximate the posterior, assuming that the generative model sufficiently approximates the prior distribution. Experimentally validated on image restoration tasks involving noisy linear and nonlinear forward operators applied to LSUN-Bedroom (256 x 256) and ImageNet (64 x 64) datasets, this approach generates high-fidelity samples with enhanced semantic diversity even under a limited number of function evaluations, offering superior efficiency and performance compared to existing diffusion-based posterior sampling techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier to create many different versions of an image or object using generative models. Generative models are like super smart computers that can create new images by mixing and matching bits from old images. But making lots of these new images is hard because the computer has to start all over again each time. The researchers came up with a way to make it easier by letting the computer play around in a special space where it can mix and match bits without having to start all over. This makes it much faster and better at creating new images that are similar to real ones. |
Keywords
» Artificial intelligence » Diffusion » Generative model
