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Hierarchical Spherical CNNs with Lifting-based Adaptive Wavelets for Pooling and Unpooling
[article]
2022
arXiv
pre-print
Pooling and unpooling are two essential operations in constructing hierarchical spherical convolutional neural networks (HS-CNNs) for comprehensive feature learning in the spherical domain. Most existing models employ downsampling-based pooling, which will inevitably incur information loss and cannot adapt to different spherical signals and tasks. Besides, the preserved information after pooling cannot be well restored by the subsequent unpooling to characterize the desirable features for a
arXiv:2205.15571v1
fatcat:gnplofies5df7pzpbhkhpipzl4