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For use when discussing the commutative and linear, but not associative operator interpreted on functions and distributions.

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1x1 Convolution. How does the math work?

Let's go back at normal convolution: let's say you have a 28x28x3 image (3 = R,G,B). I don't use torch, but keras, but the principle applies I think. … The same happens when, after a first layer of convolution with 100 filters, you obtain an image of size 28x28x100, at the second convolution layer you decide only the first two dimension of the filter, …
Francesco Pegoraro's user avatar