Summary of Neural Isometries: Taming Transformations For Equivariant Ml, by Thomas W. Mitchel et al.
Neural Isometries: Taming Transformations for Equivariant ML
by Thomas W. Mitchel, Michael Taylor, Vincent Sitzmann
First submitted to arxiv on: 29 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR); Machine Learning (cs.LG)
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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 introduces Neural Isometries, an autoencoder framework for learning representations that are equivariant to symmetries. The approach regularizes the latent space to preserve isometries, similar to rigid-body transformations, allowing for effective self-supervised representation learning. The method achieves results comparable to handcrafted networks designed for complex symmetry handling. Furthermore, the isometric maps can be used to regress camera poses directly from adjacent views of a scene. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to learn representations that are good at understanding symmetries in 3D vision tasks. It does this by creating an autoencoder that learns to map observations to a special kind of space where the mapping is related to how objects move and change in the real world. This helps it learn good representations without needing lots of labeled data. The method can even be used to figure out camera poses just from looking at nearby views of a scene. |
Keywords
» Artificial intelligence » Autoencoder » Latent space » Representation learning » Self supervised
