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Discovering Symmetry Groups with Flow Matching
arXiv – CS AI|Yuxuan Chen, Jung Yeon Park, Floor Eijkelboom, Jianke Yang, Jan-Willem van de Meent, Lawson L. S. Wong, Robin Walters||3 views
🤖AI Summary
Researchers introduce LieFlow, a machine learning framework that automatically discovers symmetries in data by treating symmetry discovery as a distribution learning problem on Lie groups. The approach can identify both continuous and discrete symmetries within a unified framework, significantly outperforming existing methods like LieGAN in experiments on synthetic and real datasets.
Key Takeaways
- →LieFlow reframes symmetry discovery as a distribution learning problem on Lie groups rather than searching for symmetry generators.
- →The framework can discover both continuous and discrete symmetries within a unified approach without assuming fixed Lie algebra basis.
- →Experiments on synthetic 2D/3D point clouds and ModelNet10 demonstrate accurate symmetry discovery capabilities.
- →LieFlow significantly outperforms the state-of-the-art baseline LieGAN, particularly in identifying discrete symmetries.
- →The approach operates directly in group space by modeling symmetry distributions over large hypothesis groups.
#machine-learning#symmetry-discovery#lie-groups#flow-matching#computer-vision#deep-learning#research#arxiv
Read Original →via arXiv – CS AI
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