I was going through this awesome research paper and I have found the term Non-Reference Loss Functions. Can someone help me to understand what it is? Some resource link is more than enough, I have googled this and I have found no clue.
What is this Non-Reference loss function and how they are training a model without paired or unpaired data? Paper PDF
Any help is appreciated.
Basically, "Non-Reference loss function" is a fancy title for "Unsupervised learning".
The authors of the paper were able to define a loss function (sec. 3.3) that describes how a "good looking image" should look like without using a "clean reference" image: The four loss terms they defined compare the output image Y to the input image I and check that the contrast in Y and it's exposure is better than I, yet boundaries, colorness and spatial consistency remains.
By defining a loss function that does not requires a "ground truth" image allows the authors to train their model on "corrupt" images only - which are much easier to come by.