248 citations · 769 across the 15 of their papers we have counts for
4 papers · 1 filter
A General Theory of Equivariant CNNs on Homogeneous Spaces
Taco Cohen, Mario Geiger, Maurice Weiler
We present a general theory of Group equivariant Convolutional Neural Networks (G-CNNs) on homogeneous spaces such as Euclidean space and the sphere. Feature maps in these networks…
Explorations in Homeomorphic Variational Auto-Encoding
Luca Falorsi, Pim de Haan, Tim R. Davidson +4
The manifold hypothesis states that many kinds of high-dimensional data are concentrated near a low-dimensional manifold. If the topology of this data manifold is non-trivial, a co…
3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
Maurice Weiler, Mario Geiger, Max Welling +2
We present a convolutional network that is equivariant to rigid body motions. The model uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and equi…
Intertwiners between Induced Representations (with Applications to the Theory of Equivariant Neural Networks)
Taco S. Cohen, Mario Geiger, Maurice Weiler
Group equivariant and steerable convolutional neural networks (regular and steerable G-CNNs) have recently emerged as a very effective model class for learning from signal data suc…