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1/ To hopefully reduce the need for emails on this in the future: Yes, we 100% cherry pick results to post colorizations on Twitter from DeOldify. Furthermore, the research models we have often produce great cherry picked results but aren't reliable enough for general use.
2/ Quite frankly it has been much easier to produce a model that is capable of creating stunning cherry picked examples than one that reliable produces good examples! That might sound counterintuitive but that's the nature of the beast.
3/ Out of dealing with this, I've definitely emerged with the mindset that I strongly favor "human in the loop" approaches to "AI art". And there's that last 5% that will probably take 95% of the effort to close the gap on just in terms of "ground truth".
Actually, that sounds just about right. I've got a lot of art models that make only a few stunning examples. Thanks for sharing.
Just out of curiosity, would be a. Technically a good approach and b. Practical, to retrain a model for a specific project to create a higher % of good results. Eg. for pictures of WW2, where the color of uniforms and military vehicles is quite distinct from other (later) events
Not to mention specific cultural events or traditional dress of specific countries, which often have their own color patterns.
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