Summary of Scalable Wasserstein Gradient Flow For Generative Modeling Through Unbalanced Optimal Transport, by Jaemoo Choi et al.
Scalable Wasserstein Gradient Flow for Generative Modeling through Unbalanced Optimal Transport
by Jaemoo Choi, Jaewoong Choi, Myungjoo Kang
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 Wasserstein Gradient Flow (WGF) is a promising approach for optimizing probability distributions. Numerical approximations require the time discretization method, typically the JKO scheme. However, this approach has quadratic training complexity, limiting scalability. The paper introduces Semi-dual JKO (S-JKO), reducing training complexity to O(K). S-JKO significantly outperforms existing WGF-based generative models on CIFAR-10 and CelebA-HQ-256 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wasserstein Gradient Flow is a new way to optimize probability distributions. Right now, it’s hard to use because the computer gets stuck in loops. The paper finds a solution by changing how the computer does this optimization. This makes it faster and better at making pictures like dogs or people. |
Keywords
* Artificial intelligence * Optimization * Probability
