Hello all,
I need to capture the correlation between the channels for my task. I was wondering if depth-wise separable convolution is able to do so. As far as I know, it just takes the number of input and output channels and I am not sure if it is able to capture dependencies between the channels at different receptive fields with different kernel sizes. Do you guys suggest any other forms of convolution to do so? Why don’t people simply use another convolution for this task in well-known architectures as they do for temporal or spatial subspaces?