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Found 5 papers, 5 papers with code
Audio classification with Dilated Convolution with Learnable Spacings
Dilated convolution with learnable spacings (DCLS) is a recent convolution method in which the positions of the kernel elements are learned throughout training by backpropagation.
Learning Delays in Spiking Neural Networks using Dilated Convolutions with Learnable Spacings
In SNNs, delays refer to the time needed for one spike to travel from one neuron to another.
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Audio Classification
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Dilated Convolution with Learnable Spacings: beyond bilinear interpolation
Dilated Convolution with Learnable Spacings (DCLS) is a recently proposed variation of the dilated convolution in which the spacings between the non-zero elements in the kernel, or equivalently their positions, are learnable.
Drastically Reducing the Number of Trainable Parameters in Deep CNNs by Inter-layer Kernel-sharing
However, most SOTA networks are too large for edge computing.
Dilated convolution with learnable spacings
We call this method "Dilated Convolution with Learnable Spacings" (DCLS) and generalize it to the n-dimensional convolution case.
