Summary of Go with the Flow: Fast Diffusion For Gaussian Mixture Models, by George Rapakoulias et al.
Go With the Flow: Fast Diffusion for Gaussian Mixture Models
by George Rapakoulias, Ali Reza Pedram, Panagiotis Tsiotras
First submitted to arxiv on: 12 Dec 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 This paper proposes a novel method for computing Schrödinger Bridges, which are diffusion processes used to steer an initial distribution to a final one while minimizing a cost functional. The authors introduce an analytic parametrization of feasible policies for steering Gaussian Mixture Models (GMMs) and show that the optimal policy can be approximated as the solution of a low-dimensional linear program. This approach generalizes naturally to more complex dynamical systems, such as controllable Linear Time-Varying systems. The authors demonstrate the potential of this method in image-to-image translation tasks and benchmark its performance on an Entropic Optimal Transport problem, showing that it outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding a way to move from one picture to another by using a special kind of math called Schrödinger Bridges. Normally, these bridges are hard to compute and require lots of training data. But the authors have come up with a new method that can solve this problem more efficiently. They show how their approach works well for certain types of images and even outperforms other methods in some cases. |
Keywords
» Artificial intelligence » Diffusion » Translation
