Summary of Generator Matching: Generative Modeling with Arbitrary Markov Processes, by Peter Holderrieth et al.
Generator Matching: Generative modeling with arbitrary Markov processes
by Peter Holderrieth, Marton Havasi, Jason Yim, Neta Shaul, Itai Gat, Tommi Jaakkola, Brian Karrer, Ricky T. Q. Chen, Yaron Lipman
First submitted to arxiv on: 27 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 introduces Generator Matching, a framework for generative modeling using arbitrary Markov processes. The approach involves constructing conditional generators to generate single data points, then learning to approximate the marginal generator which generates the full data distribution. This method unifies various generative modeling methods, including diffusion models, flow matching, and discrete diffusion models. Moreover, it expands the design space to new and unexplored Markov processes such as jump processes. The framework also enables the construction of superpositions of Markov generative models and multimodal models in a rigorous manner. Empirical validation is provided through image and multimodal generation tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to generate fake data that looks real, like images or sounds. It uses something called Markov processes, which are used in lots of different areas like math and science. The new method is good at generating both single pieces of data and whole collections of data. It’s also really flexible and can be used to create things like super-realistic images or music that combines different styles. |
Keywords
» Artificial intelligence » Diffusion
