Sorry, I may have spoken in a misleading way. I am not looking to find the inverse of a specific convolution. I’m looking for a layer which performs the opposite of a convolution layer. That is, a layer which will accept an input of height x width x in_channels, move across each spatial element being accepted as a 1 x 1 x in_channels input, with each of these outputting a kernel x kernel x out_channels patch, and combining those patches to form a final total output. This can of course also be represented as a similar matrix multiplication as the ordinary convolutions. Is there a reason such a layer would not be an option?