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I'm wondering whether HexagDLy could be used for processing of spherical images by interpolating the weights of networks trained on perspective images.
In Orientation-Aware Semantic Segmentation on Icosahedron Spheres it is mentioned that "since our kernels operate on the tangent of the sphere, standard feature weights, pretrained on perspective data, can be directly transferred with only small need for weight refinement".
Has there been experiments swapping nn.Conv2D with hexagdly.Conv2d with the "transfer" of convolution kernels weights from one euclidian to hexagonally sampled manifolds ?
The text was updated successfully, but these errors were encountered:
I'm wondering whether HexagDLy could be used for processing of spherical images by interpolating the weights of networks trained on perspective images.
In Orientation-Aware Semantic Segmentation on Icosahedron Spheres it is mentioned that "since our kernels operate on the tangent of the sphere, standard feature weights, pretrained on perspective data, can be directly transferred with only small need for weight refinement".
Same in Tangent Images for Mitigating Spherical Distortion
a similar idea is used "we show that we can transfer networks trained on perspective images to spherical data without fine-tuning"
Seeing Hexagonal Convolutional Neural Networks for
Hexagonal Grids, I would assume we can also sample, interpolate and realigned for efficient processing ?
Has there been experiments swapping
nn.Conv2D
withhexagdly.Conv2d
with the "transfer" of convolution kernels weights from one euclidian to hexagonally sampled manifolds ?The text was updated successfully, but these errors were encountered: