Summary of Scorefusion: Fusing Score-based Generative Models Via Kullback-leibler Barycenters, by Hao Liu et al.
ScoreFusion: fusing score-based generative models via Kullback-Leibler barycenters
by Hao Liu, Junze Tony Ye, Jose Blanchet, Nian Si
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 Medium Difficulty Summary: We introduce ScoreFusion, a novel method for fusing multiple pre-trained diffusion models that generate from auxiliary populations. This approach is particularly useful when there’s limited observed data on the target population. Our starting point considers the family of KL barycenters of the auxiliary populations, which we prove to be an optimal parametric class in the KL sense. By recasting the learning problem as score matching in denoising diffusion, we obtain a tractable way of computing the optimal KL barycenter weights. We also derive a dimension-free sample complexity bound in total variation distance, provided that the auxiliary models are well fitted for their own task and the combined tasks capture the target well. Our fusion method differs from checkpoint merging in AI art creation, allowing generation of new populations, as we illustrate in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: We’re introducing a new way to combine multiple artificial intelligence (AI) models that generate images or sounds based on what they’ve learned from different sources. This is helpful when there’s not much data available on the type of image or sound you want to create. Our approach uses a combination of mathematical concepts and machine learning techniques to find the right mix of information from these different sources. We show that our method can be used to generate new types of images or sounds, which we demonstrate with experiments. |
Keywords
» Artificial intelligence » Diffusion » Machine learning
