Summary of Stochastic Sampling From Deterministic Flow Models, by Saurabh Singh et al.
Stochastic Sampling from Deterministic Flow Models
by Saurabh Singh, Ian Fischer
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (stat.ML)
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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 approach to deterministic flow models by transforming their underlying ordinary differential equations (ODEs) into families of stochastic differential equations (SDEs). This method allows for the creation of stochastic samplers that continuously span the spectrum of deterministic and stochastic sampling, given access to the flow field and score function. The proposed method alleviates issues with deterministic samplers and empirically outperforms them on both a toy Gaussian setup and the large-scale ImageNet generation task. Additionally, the family of stochastic samplers provides an extra knob for controlling the diversity of generated samples. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper takes a step forward in machine learning by turning ordinary differential equations into something called stochastic differential equations. This helps create new ways to generate images or data that are more diverse and better than before. The authors tested their idea on small and large datasets and showed it works well. They also discovered that they can control the diversity of generated samples, which is exciting for applications like image generation. |
Keywords
* Artificial intelligence * Image generation * Machine learning
