arXiv:2207.03116v4 Announce Type: replace Abstract: We introduce a general method for learning representations that are equivariant to symmetries of data. Our central idea is to decompose the latent space into an invariant factor and the symmetry group itself. The components semantically correspond to intrinsic data classes and poses respectively. The learner is trained on a loss encouraging equivariance based on supervision from relative symmetry information. The approach is motivated by theoretical results from group theory and guarantees representations that are lossless, interpretable and
The paper introduces a novel and robust method for learning equivariant representations, addressing a core challenge in making AI systems more interpretable and robust to data variations.
This work is critical for advancing AI robustness and interpretability, particularly for applications where understanding symmetry and intrinsic data properties is essential, such as in robotics, scientific discovery, and object recognition.
The proposed 'Class-Pose Decomposition' provides a principled way to decompose latent spaces into invariant and symmetry group factors, yielding more lossless and interpretable representations.
- · AI researchers
- · Robotics developers
- · Computer vision companies
- · Scientific computing platforms
- · Opaque AI systems
- · AI models sensitive to spurious correlations
Improved performance and reliability of AI systems in tasks requiring spatial reasoning or understanding of transformations.
Faster development and deployment of intelligent agents that can generalize across different orientations and contexts.
Acceleration of scientific discovery by enabling AI to uncover fundamental symmetries in complex data from physics or biology.
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Read at arXiv cs.LG