My main concern is about how would one decide the appropriate normalization values for the input images. Is the safest way, to run the experiments with all the available combinations, i.e tanh activation with values in [0,1] and [-1,1], and decide based on the crispness of the images?
Furthermore, if some special losses are used (pre-trained VGG like style, perceptual), these networks were trained with images normalized in the range [0,1] so another confusion occurs.
In any case, as I understand from your answer, do you mean that the network’s “power” is too strong and alleviates all the “mismatches” and finally learns the appropriate weights regardless of the image scales?