Re:PixelNN – Example-Based Image Synthesis
by trevyn on Monday September 25, 2017 @06:00AM
(#15329145)
> The catch is that you need to know that the target image comes from roughly the same distribution as the training set.
When humans think about "enhance", they imagine extracting subtle details that were not obvious from the original, which implies that they know very little about what distribution the original image comes from. If they did, they wouldn't have a need for "enhance" 99% of the time -- the remaining 1% is for artistic purposes, which this is indeed suited for.
It'll be interesting to see how society copes with the removal of the "photographs = evidence" prior.
> when enhancing celebrity images, if the model is able to figure out who is in the picture this massively decreases the set of plausible outputs.
When humans think about "enhance", they imagine extracting subtle details that were not obvious from the original, which implies that they know very little about what distribution the original image comes from. If they did, they wouldn't have a need for "enhance" 99% of the time -- the remaining 1% is for artistic purposes, which this is indeed suited for.
It'll be interesting to see how society copes with the removal of the "photographs = evidence" prior.
> when enhancing celebrity images, if the model is able to figure out who is in the picture this massively decreases the set of plausible outputs.
This is an excellent insight.