Papers › Equivariant Frames and the Impossibility of Continuous Canonicalization

Equivariant Frames and the Impossibility of Continuous Canonicalization

25 Feb 2024arXiv:2402.16077archive 2025-07-28

Nadav Dym, Hannah Lawrence, Jonathan W. Siegel

Canonicalization provides an architecture-agnostic method for enforcing equivariance, with generalizations such as frame-averaging recently gaining prominence as a lightweight and flexible alternative to equivariant architectures. Recent works have found an empirical benefit to using probabilistic frames instead, which learn weighted distributions over group elements. In this work, we provide strong theoretical justification for this phenomenon: for commonly-used groups, there is no efficiently computable choice of frame that preserves continuity of the function being averaged. In other words, unweighted frame-averaging can turn a smooth, non-symmetric function into a discontinuous, symmetric function. To address this fundamental robustness problem, we formally define and construct \emph{weighted} frames, which provably preserve continuity, and demonstrate their utility by constructing efficient and continuous weighted frames for the actions of SO(2), SO(3), and Sₙ on point clouds.

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one_hot jwsiegel2510/sn-invariant-weighted-frames/point_cloud_mnist_classifier.py official repository ran · fixture could not drive it no licence file found · pointer only · 60fb650d6d546a2c · report
process_image_to_point_cloud jwsiegel2510/sn-invariant-weighted-frames/generate_point_cloud_MNIST.py official repository ran · our draft was wrong no licence file found · pointer only · 7c7f60d15347ce16 · report
random_conv_layer_params jwsiegel2510/sn-invariant-weighted-frames/point_cloud_mnist_classifier.py official repository ran · our draft was wrong no licence file found · pointer only · 292e492e20f9bdaa · report
random_layer_params jwsiegel2510/sn-invariant-weighted-frames/point_cloud_mnist_classifier.py official repository ran · our draft was wrong no licence file found · pointer only · 6917ff7f6005e43d · report

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