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Found 4 papers, 2 papers with code
Moving Frame Net: SE(3)-Equivariant Network for Volumes
Equivariance of neural networks to transformations helps to improve their performance and reduce generalization error in computer vision tasks, as they apply to datasets presenting symmetries (e. g. scalings, rotations, translations).
Scale Equivariant U-Net
Therefore, this paper introduces the Scale Equivariant U-Net (SEU-Net), a U-Net that is made approximately equivariant to a semigroup of scales and translations through careful application of subsampling and upsampling layers and the use of aforementioned scale-equivariant layers.
Differential invariants for SE(2)-equivariant networks
Symmetry is present in many tasks in computer vision, where the same class of objects can appear transformed, e. g. rotated due to different camera orientations, or scaled due to perspective.
Scale Equivariant Neural Networks with Morphological Scale-Spaces
The translation equivariance of convolutions can make convolutional neural networks translation equivariant or invariant.
