ML systems are biased when data is biased. This face upsampling system makes everyone look white because the network was pretrained on FlickFaceHQ, which mainly contains white people pics. Train the *exact* same system on a dataset from Senegal, and everyone will look African.https://twitter.com/bradpwyble/status/1274380641644294150 …
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The most efficient way to do it though is to equalize the frequencies of categories of samples during training. This forces the network to pay attention to all the relevant features for all the sample categories.
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Thank you. Question on extremes: doesn’t this imply if I have only one Inuit woman in my dataset, I can’t exactly equalize. Further thought: aren’t humans continuous? 1/2
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To do this properly; wouldn’t we need to sequence everyone’s DNA? Plot by maternal and paternal DNA markers?
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