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Deep Parametric Continuous Convolutional Neural Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Standard convolutional neural networks assume a grid structured input is available and exploit discrete convolutions as their fundamental building blocks. This limits their applicability to many real-world applications. In this paper we propose Parametric Continuous Convolution, a new learnable operator that operates over non-grid structured data. The key idea is to exploit parameterized kernel functions that span the full continuous vector space. This generalization allows us to learn over
doi:10.1109/cvpr.2018.00274
dblp:conf/cvpr/WangSMPU18
fatcat:augqqmf56vcqtcof3bi2x64m6e