I haven't revealed -too- much about the new DeOldify model I'm working on yet. But two things it does a lot better with are crowds and buildings. Behold!https://twitter.com/citnaj/status/1191060069854543873 …
Obsessively pursuing the perfection of image and video colorization/restoration using deep learning. Creator of DeOldify.
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Jason Antic Retweeted Jason Antic
I haven't revealed -too- much about the new DeOldify model I'm working on yet. But two things it does a lot better with are crowds and buildings. Behold!https://twitter.com/citnaj/status/1191060069854543873 …
Jason Antic added,
Super impressive work, but, the colors of the coats are a bit too uniform. I wonder if having an additional loss about diversity within patches might help?
Still trying to wrap my head around it honestly. I can tell you this much: A lot of diversity issues simply had to do with model capacity and attention being too limited. So I'll continue perusing that relatively low hanging fruit until that's exhausted.
I'd love to read a mind dump of how you go about improving / tweaking your models. With NNs, it feels like there's an endless frontier of possible paths and it's really hard to know when a particular approach might work if you put more effort and when it's a dead end.
Endless experimentation/heating up the house coupled with constantly questioning my previous conclusions/sanity lol
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