SEIS defines subspace-based scores to measure layer-wise equivariance and invariance in neural representations under geometric transformations.
Group equivariant con- volutional networks
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Rotation-equivariant convolutions in deformable brain MRI registration networks deliver higher accuracy with fewer parameters, greater robustness to rotations, and better performance on limited training data.
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SEIS: Subspace-based Equivariance and Invariance Scores for Neural Representations
SEIS defines subspace-based scores to measure layer-wise equivariance and invariance in neural representations under geometric transformations.
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Rotation Equivariant Convolutions in Deformable Registration of Brain MRI
Rotation-equivariant convolutions in deformable brain MRI registration networks deliver higher accuracy with fewer parameters, greater robustness to rotations, and better performance on limited training data.