In some of computer vision papers I read that they start off with a bigger sized image and use pooling to reduce dimensionality and train on the image with lower resolution. However, they don't mention how they deal with the size difference. If we want to have pixel wise estimation, then this should be an issue, is it not?
For example in the "Stacked Hourglass" paper , they use images of shape (256, 256, 3) but their final output resolution is size (64, 64). This, according to the paper, "does not affect the network’s ability to produce precise joint predictions."
How can we make predictions for the original size if our final output size is 4 times smaller? Do we upsample again after getting our final prediction back to our original size?