Do all filters of the same convolutional layer need to have the same dimensions and stride? In Convolutional Neural Networks, do all filters of the same convolutional layer need to have the same dimensions and stride?
If they don't, then it would seem the channel produced by each filter would have different sizes. Or is there some way to get around that?
It seems that a similar question has been raised here: https://stackoverflow.com/questions/57438922/different-size-filters-in-the-same-layer-with-tensorflow-2-0
Like answered in the link above, you could combine severall Conv2D ops with different kernel sizes on the same input. You would have to adapt each output with padding, or cropping, so that you could concatenate all of them.
Hope this helps!