Summary of A Probabilistic Approach to Learning the Degree Of Equivariance in Steerable Cnns, by Lars Veefkind et al.
A Probabilistic Approach to Learning the Degree of Equivariance in Steerable CNNs
by Lars Veefkind, Gabriele Cesa
First submitted to arxiv on: 6 Jun 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 probabilistic method to learn the degree of equivariance in steerable convolutional neural networks (SCNNs), which enhances task performance by modelling geometric symmetries. The authors parameterise the degree of equivariance as a likelihood distribution over the transformation group using Fourier coefficients, allowing for layer-wise and shared equivariance. This flexible framework is applicable to various types of equivariant networks and enables learning equivariance with respect to any subgroup of any compact group without requiring additional layers. Experiments demonstrate competitive performance on datasets with mixed symmetries, with learned likelihood distributions representative of the underlying degree of equivariance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers learn better by figuring out how much symmetry they need in a special kind of artificial intelligence called steerable convolutional neural networks (SCNNs). Symmetry is important because it makes the computer’s decisions more accurate. But, it can be tricky to know exactly how much symmetry is needed. The researchers found a way to make this process easier by turning the degree of symmetry into a mathematical probability problem. This allows the computer to learn about different types of symmetry and use that knowledge to improve its performance. |
Keywords
» Artificial intelligence » Likelihood » Probability
