Today someone asked on Google+
Hello, when computing the gradients CNN, the weights need to be rotated, Why ?
I had the same question when I was pouring through code back in the day, so I wanted to clear it up for people once and for all.
Simple answer:
This is just a efficient and clean way of writing things for:
Computing the gradient of a valid 2D convolution w.r.t. the inputs.
There is no magic here
Here’s a detailed explanation with visualization!
Input
Kernel
Output
Section 1: valid convolution (input, kernel)
Section 2: gradient w.r.t. input of valid convolution (input, kernel) = weighted contribution of each input location w.r.t. gradient of Output
Section 3: full convolution(180 degree rotated filter, output)
As you can see, the calculation for the first three elements in section 2 is the same as the first three figures in section 3.
Hope this helps!