Summary of (deep) Generative Geodesics, by Beomsu Kim and Michael Puthawala and Jong Chul Ye and Emanuele Sansone
(Deep) Generative Geodesics
by Beomsu Kim, Michael Puthawala, Jong Chul Ye, Emanuele Sansone
First submitted to arxiv on: 15 Jul 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 The proposed research introduces a novel Riemannian metric for assessing similarity between data points generated by various generative models. This agnostic metric only requires evaluating the likelihood of each model in the data space and leads to the concepts of generative distances and geodesics, which can be efficiently computed. The authors demonstrate three applications: clustering, data visualization, and interpolation, providing new tools for understanding the geometrical properties of generative models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how different machine learning models create patterns in data. It develops a way to measure how similar these patterns are. This helps us understand how these models work and can be used to group similar data points together, visualize complex data sets, or fill in missing values. The goal is to provide new insights into the behavior of generative models. |
Keywords
» Artificial intelligence » Clustering » Likelihood » Machine learning
