Corpus ID: 131774030

DeepSphere: towards an equivariant graph-based spherical CNN

@article{Defferrard2019DeepSphereTA,
  title={DeepSphere: towards an equivariant graph-based spherical CNN},
  author={Micha{\"e}l Defferrard and Nathanael Perraudin and T. Kacprzak and R. Sgier},
  journal={ArXiv},
  year={2019},
  volume={abs/1904.05146}
}
  • Michaël Defferrard, Nathanael Perraudin, +1 author R. Sgier
  • Published 2019
  • Computer Science, Mathematics
  • ArXiv
  • Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical convolutions. As equivariance is desired to exploit rotational symmetries, we discuss how to approach rotation equivariance using the graph neural network introduced in Defferrard et al. (2016). Experiments show good performance on rotation… CONTINUE READING

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