Implement inhibition where width and damping of the wavelet are the learned parameters. We may benchmark to versions: Firstly we can simply use the Ricker wavelet equation to obtain the filter values and add its parameters to the module. For this we need to implement our own Ricker wavelet function in PyTorch. Secondly we can approximate the Wavelet by the difference of two Gaussians, which might be faster during backpropagation. For the latter, to still only have two parameters, use the difference of standard deviations as parameter, rather than two stds.
Implement inhibition where width and damping of the wavelet are the learned parameters. We may benchmark to versions: Firstly we can simply use the Ricker wavelet equation to obtain the filter values and add its parameters to the module. For this we need to implement our own Ricker wavelet function in PyTorch. Secondly we can approximate the Wavelet by the difference of two Gaussians, which might be faster during backpropagation. For the latter, to still only have two parameters, use the difference of standard deviations as parameter, rather than two stds.