simulated-unsupervised-tensorflow
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Motivation behind the denormalize function
The denormalize function in layers.py is defined as (layer + 1.)/2. If the aim is to revert the earlier normalization, shouldn't we have (layer + 1.)*127.5?
Asking because I'm facing a problem where the refined images are extremely dark (low intensity), since the pixel values are very low after denormalization.