Summary of Combostoc: Combinatorial Stochasticity For Diffusion Generative Models, by Rui Xu et al.
ComboStoc: Combinatorial Stochasticity for Diffusion Generative Models
by Rui Xu, Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Shiqing Xin, Changhe Tu, Taku Komura, Wenping Wang
First submitted to arxiv on: 22 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR)
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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 paper investigates the impact of combinatorial complexity on diffusion generative models, demonstrating that existing training schemes insufficiently sample the space spanned by dimension combinations. This results in degraded test-time performance. The authors propose a simple fix, ComboStoc, which constructs stochastic processes to fully exploit combinatorial structures. ComboStoc accelerates network training across diverse data modalities, including images and 3D shapes, and enables novel test-time generation techniques with varying degrees of control. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how diffusion generative models can be improved by considering the way different attributes are combined in high-dimensional data. The authors show that existing methods don’t fully take into account this complexity, which makes them less effective. They suggest a simple solution called ComboStoc, which helps train models faster and more effectively for various types of data. This could lead to new ways of generating data at test time. |
Keywords
» Artificial intelligence » Diffusion
