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Any better metrics that you might not have come up with along the way? Or sets of metrics?
2/ ... learning tasks. Specifically- the larger and more varied the training, the better. This is basically the Facebook WSL models' secret ingredient too. I think everything else is basically noise that you have to sift through with experiments.
A recent paper by Karras also seems to suggest that for transfer learning of StyleGAN models diversity of training data is really important too.https://twitter.com/Buntworthy/status/1271554286917468161 …
Is there an established way to measure how "varied" a training dataset is?
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