[sytelus]

If you had like to brutely honest, you should put randomly selected set, along with the hand-picked set - labeling each set how it was selected. This is a cancer in current deep learning research. You see paper with such a glowing cool examples but in reality they are just hiding all problematic cases while being fully aware of it. If this happened anywhere else in any other domain people would say they got ripped off and outright lied to.

[citnaj]

I understand the frustration and in fact share it to a certain extent with science in general. Keep in mind that this wasn't intended to be published as a paper or anything like that. I'm just a software engineer who picked a problem and found a pretty cool solution.

Primarily I thought it was cool because it should be useful in many other image modification domains. And then it blew up in popularity today (didn't expect that). But yeah in the notes in the readme at github I do say this:

>To expand on the above- Getting the best images really boils down to the art of selection.

I added that after getting some feedback similar to yours, because before that, this disclaimer wasn't quite cutting it apparently:

>You'll have to play around with the size of the image a bit to get the best result output.

So yeah I'm trying to stay honest here. I'm not going as far as picking completely random samples, admittedly, but really what I'm trying to drive at here is you can produce cool results with this tool. It's not perfect, but it's a tool. And even if you pick at random, they still look pretty damn good. Just sometimes it renders the tv as color and sometimes it doesn't, and i picked the cool option.

[citnaj]

I should add too, just to be clear- I'm not at all involved in academia.