Summary of Improved Canonicalization For Model Agnostic Equivariance, by Siba Smarak Panigrahi et al.
Improved Canonicalization for Model Agnostic Equivariance
by Siba Smarak Panigrahi, Arnab Kumar Mondal
First submitted to arxiv on: 23 May 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 This paper introduces a novel approach to achieve architecture-agnostic equivariance in deep learning, addressing limitations of traditional layerwise equivariant architectures and existing methods. The authors propose an optimization-based method that employs any non-equivariant network for canonicalization using contrastive learning. This approach efficiently learns a canonical orientation, offering more flexibility for the choice of canonicalization network. Empirical results demonstrate that this method outperforms existing approaches in achieving equivariance for large pre-trained models and significantly accelerates the canonicalization process, making it up to 2 times faster. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make deep learning models work equally well on different shapes or structures. Right now, we have two ways to do this: one that requires designing special versions of existing models and training them from scratch, which is very impractical. The other way uses something called canonicalization, but it needs very powerful and expensive computers to work accurately. The authors propose a new method that can use any computer program for canonicalization, making it faster and more flexible. They tested this approach and found that it works better than the existing methods and takes less time. |
Keywords
» Artificial intelligence » Deep learning » Optimization
