Summary of Diffusion Rejection Sampling, by Byeonghu Na et al.
Diffusion Rejection Sampling
by Byeonghu Na, Yeongmin Kim, Minsang Park, Donghyeok Shin, Wanmo Kang, Il-Chul Moon
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper introduces a novel method called Diffusion Rejection Sampling (DiffRS) to improve the sampling performance under well-trained diffusion models. By aligning the sampling transition kernels with the true ones at each timestep, DiffRS can achieve a tighter bound on sampling error compared to pre-trained models. Theoretical analysis confirms this improvement, and empirical results demonstrate state-of-the-art performance on benchmark datasets for fast diffusion samplers and large-scale text-to-image diffusion models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion Rejection Sampling is a new way to make better images or text using computers. It helps machines create more realistic pictures or words by making small changes at each step until it gets something good. This method works better than others because it checks the quality of what’s being created and makes adjustments accordingly. The results are really impressive, showing that this method can make better pictures and text compared to other ways. |
Keywords
* Artificial intelligence * Diffusion
