Summary of Grounding Continuous Representations in Geometry: Equivariant Neural Fields, by David R Wessels et al.
Grounding Continuous Representations in Geometry: Equivariant Neural Fields
by David R Wessels, David M Knigge, Samuele Papa, Riccardo Valperga, Sharvaree Vadgama, Efstratios Gavves, Erik J Bekkers
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 proposes Equivariant Neural Fields (ENFs), a novel architecture for Conditional Neural Fields (CNFs) that leverages geometry-informed cross-attention to condition the neural field on a latent point cloud of features. The ENF architecture enables steerability, where both the field and latent representation transform accordingly if the field transforms. This equivariance relation allows for geometric reasoning in latent space and efficient learning of datasets with similar local patterns. The authors demonstrate improvements over baselines in tasks including classification, segmentation, forecasting, reconstruction, and generative modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to use Neural Fields (NeF) that takes into account the geometry or orientation of features in the data. This is important because many tasks require us to understand these geometric patterns. The authors create an Equivariant Neural Field (ENF) that uses geometry-informed cross-attention to condition the NeF on a latent point cloud of features. They show that this approach works well for tasks like classification, segmentation, and forecasting. |
Keywords
» Artificial intelligence » Classification » Cross attention » Latent space
