Summary of Wasserstein Bounds For Generative Diffusion Models with Gaussian Tail Targets, by Xixian Wang et al.
Wasserstein Bounds for generative diffusion models with Gaussian tail targets
by Xixian Wang, Zhongjian Wang
First submitted to arxiv on: 15 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Analysis of PDEs (math.AP); Numerical Analysis (math.NA)
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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 presents an estimate of the Wasserstein distance between the data distribution and score-based generative models’ generation, assuming an accurate approximation of the score and Gaussian-type tail behavior of the data distribution. The proposed method’s complexity bound in dimension is O(sqrt(d)) with a logarithmic constant, suitable for distributions with compact support or Bayesian posteriors with bounded observation operators. The paper derives corresponding convergence and complexity bounds. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research calculates how well score-based generative models match the real data distribution. It assumes that the models are very close to being perfect and that the data follows a specific pattern. The study shows that the models’ performance depends on the dimension of the data, with a logarithmic constant affecting the results. This work is relevant for modeling distributions with limited support or Bayesian inference with constrained observations. |
Keywords
* Artificial intelligence * Bayesian inference
