Summary of Symmetry Discovery Beyond Affine Transformations, by Ben Shaw et al.
Symmetry Discovery Beyond Affine Transformations
by Ben Shaw, Abram Magner, Kevin R. Moon
First submitted to arxiv on: 5 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper presents a framework for discovering continuous symmetry in data, going beyond the current state of the art in detecting affine transformations. The approach is based on the manifold assumption and is shown to be competitive with existing methods like LieGAN for large sample sizes. Additionally, it can detect symmetries beyond the affine group and is more computationally efficient than LieGAN. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores ways to improve machine learning tasks by detecting symmetry in data. It develops a new approach that can find continuous symmetry in data, which is important because current methods are limited. The method is compared to an existing one called LieGAN and is found to be good at finding affine symmetries for large datasets. It’s also better than LieGAN when working with small datasets. |
Keywords
» Artificial intelligence » Machine learning
