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Notably, @citnaj doesn’t really pay attention to loss or eval metrics for DeOldify. The only way to test it is to look at it and see.
I think this idea is upsetting to people who want to believe everything can be automated. (It upsets me.) But it seems true.
Yeah even FID, which I've been using lately, has its limits. lt's helpful and clearly pretty sophisticated but when I still have to verify the results with my own perception- things are still missed by that magic number.
The kind of ad hoc analysis you'd do with DeOldify/GPT-2 is (a) not feasible for the general problem space and (b) impossible to scale without reference to _some_ kind of metric. Absolutely agree that we should be wary of our metrics, but they seem like the only game in town!
DeOldify and GPT-2 are two examples of the general problem space. They’re typical, not special. As for reference to some metric, I think people get confused because loss is how a model improves. Loss isn’t quality, though. That can’t be measured except with human tests.
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