Hey thanks for the question. Regarding your first question: that was exactly what I wondered, and I found out that they don't necessarily overfit to the type o

Hey thanks for the question. Regarding your first question: that was exactly what I wondered, and I found out that they don't necessarily overfit to the type of degradation (the repo shows a section called "extreme super resolution" where the input is already high res, and the output shows no artifacts). But they do tend to overfit to the domain of images. So you would probably observe artifacts running this on pictures of...curcuit boards? But those are also functions of batch norm, as some literature points out, and I tried to mitigate this problem by not using batch norm layers in the end of the network. I always welcome people testing it out and reporting any problems they observe though. For the second question: I would say definitely. You don't need to waste a lot of time worrying about data quality, or even annotation in this case. Also, ground truth to the model is actual, real, without noise ground truth, so they may be able to learn better features representative of the distribution of data. I'm sure there's an argument to be made here about the generalization capability of models being trained on noisy data being higher, but I personally think cleaner data = better data. Especially for a GAN where the goal is to learn the distribution of your input space. Noise would only hinder that.