Re:Amnesia – High-Accuracy Data Anonymization
by levesque on Wednesday December 02, 2020 @10:57AM
(#25277538)
You could kickstart training on simulators and then do a transfer, i.e. make adjustments to your final model, on real world data. But to learn only on generated data the problem boils down to the nonparametric features you will be using to state that the generated data is similar to the real data. What is a complex enough feature to say that images are equivalent? They might be statistically equivalent according to your features, but are they really? I think this is a very hard problem, because if we did have a good answer to this question then Tesla & co. would already be training their models on perfect simulators and we wouldn't see the glitches currently found in autonomous driving applications.
Re:Amnesia – High-Accuracy Data Anonymization
by fractionalhare on Wednesday December 02, 2020 @01:07PM
(#25279284)
That's what I figured, re: transfer modeling. Thanks for chiming in.
Theoretically speaking if the generated data has the same distribution and parameters as the real data [1], and encodes similar nonparametric features like seasonality and user activity, I think generated data might be fine. [2]
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1. Admittedly tricky if you have limited data and no insight into the underlying population distribution/features, just those of the sample. But then you have a worse problem for modeling diagnostics anyway.
2. In the sense that anything is "fine", which is a spectrum that requires some critical skepticism in statistics. There are always caveats but it may still be robust and useful.