As I see more of these machine learning applications, where a neural network is trained on a dataset, then applied to a similar dataset, I wonder how well it wo

As I see more of these machine learning applications, where a neural network is trained on a dataset, then applied to a similar dataset, I wonder how well it would help if the network was trained on part of the new dataset as well, as some kind of "specialization". One could take a generic "AI Gigapixel" net, and retrain it on some of the newer Star Trek content, for which an upscaled/remastered "ground truth" exists. My guess is that it would help a lot with features that are specific to the Star Trek universe. Taking this further, one could take the resulting samples, and "rate" them, before feeding it back in the training engine. This would make some kind of "adversarial" feedback loop, but instead of the GAN, humans are involved in the loop (which could be used to train an adversarial network as well, that said). My hope is that it would converge to much better results in a shorter time, and with less input data. My apologies if those are common concepts in ML. If so, I'd love to look at some references that could further my understanding on these topics, and get me to use the right terminology.