Hello,
in the paper, in page 5, the equation 3 is referenced to 32: "Depth map prediction from a single image using a multi-scale deep network" from Eigen et. al. . But looking at this reference couldn't answer the following:
What is the reason to put the loss term in a square root there?
Sum of squares will always be $\geq$ square of sums, so the square root in this formulation will always be safe to use, but what is the underlying reason to do so?
Hello, in the paper, in page 5, the equation 3 is referenced to 32: "Depth map prediction from a single image using a multi-scale deep network" from Eigen et. al. . But looking at this reference couldn't answer the following:
What is the reason to put the loss term in a square root there?
Sum of squares will always be $\geq$ square of sums, so the square root in this formulation will always be safe to use, but what is the underlying reason to do so?
Best regards,