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Scaling Spherical Deep Learning to High-Resolution Input Data
Towards Data Science - Medium towardsdatascience.com
Scattering networks on the sphere for scalable and rotationally equivariant spherical CNNs
Conventional spherical CNNs are not scalable to high resolution classification tasks. In this post we present spherical scattering layers — a novel spherical layer that reduces the dimensionality of the input data while retaining relevant information, while also being rotationally equivariant. Scattering networks work by employing predefined convolutional filters from wavelet analysis rather than learning convolutional filters from scratch. As the weights of scattering layers are designed rather …
data deep learning geometric-deep-learning machine learning scaling sparsity thoughts-and-theory wavelet