3D Steerable CNNs: Learning Rotationally Equivariant Features in Volumetric Data
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| Authors | Wouter Boomsma, Max Welling, Maurice Weiler, Mario Geiger, Taco Cohen |
| Journal/Conference Name | NeurIPS 2018 12 |
| Paper Category | Artificial Intelligence |
| Paper Abstract | 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 equivariant convolutions to map between such representations. These SE(3)-equivariant convolutions utilize kernels which are parameterized as a linear combination of a complete steerable kernel basis, which is derived analytically in this paper. We prove that equivariant convolutions are the most general equivariant linear maps between fields over R^3. Our experimental results confirm the effectiveness of 3D Steerable CNNs for the problem of amino acid propensity prediction and protein structure classification, both of which have inherent SE(3) symmetry. |
| Date of publication | 2018 |
| Code Programming Language | Python |
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