Well in this case they have the ground truth as comparison, so the error can be evaluated. > The resulting images look nice but there's no guarantee that all t

Well in this case they have the ground truth as comparison, so the error can be evaluated. > The resulting images look nice but there's no guarantee that all the extra detail visible in those images is genuine detail and not just "believable" data filled in by the net It depends on the error metric specified as the objective. If the network is minimizing squared error to ground truth, then it won't generate believable but untrue data, since its objective wouldn't reward this behavior. If the network is trained against adversarial distinguishability however (with the noisy prior), then this becomes a more or less inevitable issue. So both can be chosen depending on what you want: certainty that the content is semantically equivalent, or just good looking images.