Summary of Towards Faster Non-asymptotic Convergence For Diffusion-based Generative Models, by Gen Li et al.
Towards Faster Non-Asymptotic Convergence for Diffusion-Based Generative Models
by Gen Li, Yuting Wei, Yuxin Chen, Yuejie Chi
First submitted to arxiv on: 15 Jun 2023
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 This paper develops a suite of non-asymptotic theory to understand the data generation process of diffusion models in discrete time. The authors establish convergence rates for two popular samplers: a deterministic ODE-based sampler with a rate proportional to 1/T, and a stochastic DDPM-type sampler with a rate proportional to 1/√T. These results characterize how errors in estimating the Stein score functions affect the quality of the data generation processes. The theory is developed using an elementary approach without resorting to specialized toolboxes for SDEs and ODEs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Diffusion models are a type of generative model that can convert noise into new data instances. While they have been shown to be very effective, the underlying math hasn’t been fully understood until now. This paper helps fix that by developing a set of rules (theory) for how these models work in discrete time. The authors show that two popular types of samplers – one based on an ordinary differential equation (ODE), and another based on a type of probabilistic model called DDPM – can generate new data with certain levels of accuracy. This is important because it helps us understand what makes these models tick, which could lead to even better results in the future. |
Keywords
* Artificial intelligence * Diffusion * Generative model * Probabilistic model
