Quote:
Originally Posted by smok3
Define "train".
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You present your
current network with an input/output pair, the
training sample. In this case, the "input" would be a
low-resolution version of the original image, and the "output" is the original
high-resolution image. You let your network create its own (high-res) output image from the given (low-res) "input" image. And then you compare
that against the given optimal "output" image (original). Of course, there will be some difference between the network's actual output and the optimal (desired) output - especially at the beginning of the training phase. This difference, or "error", will be used to
update (improve) the network, so that the error is reduced. For example, one approach is to let the "error" propagate through the network in
backwards direction and adjust the individual weights accordingly. You repeat this process with
many training samples (input/output pairs). In the end, you get a network that (hopefully) produces good results, even for
unknown inputs.