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1/ In developing DeOldify, I've had a very helpful, yet seemingly obvious, rule of thumb to help guide figuring out improvements in training: What can be done to close the gap between training and inference? Getting critical about this can yield some seriously low hanging fruit.
2/ There's many, many things you can enumerate here. Here's a less obvious example: Are you account for jpeg artifacts? What about "moar jpeg" scenarios where you rejpeg an image to death? Turns out that's very common in the real world, of course, yet easy to overlook!
3/ This is also what lead to reshaping all images at inference as squares. Remember that training in batches is done with squares. Close the gap with inference by simulating the stretching done to "squarify" the images!
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