Hello,
I was wondering how the gradients for the loss function of , say color consistency loss, are computed wrt the weights by backprop.
You have your depth d at a coordinate which is a function of your weights, say X at a pixel location i, i.e. d(X,i) .
From this depth, you get your warped image coordinate, say W(d). For the color consistency, you compare the pixel values of the source image, and warped image, which would be some difference between img(i) and img(W(d(X,i) ) .
Numerically, I can see how you can obtain the derivative of this function wrt the weights, but how does auto-differentiation do it analytically, since img(i) cannot really be stated analytically?
Hello,
I was wondering how the gradients for the loss function of , say color consistency loss, are computed wrt the weights by backprop.
You have your depth
dat a coordinate which is a function of your weights, sayXat a pixel locationi, i.e.d(X,i).From this depth, you get your warped image coordinate, say
W(d). For the color consistency, you compare the pixel values of the source image, and warped image, which would be some difference betweenimg(i)andimg(W(d(X,i) ).Numerically, I can see how you can obtain the derivative of this function wrt the weights, but how does auto-differentiation do it analytically, since
img(i)cannot really be stated analytically?