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Learning From the Best Possible Measurements
When we talk about training a network, we still want to optimise a metric los licon standard el tulos. And the strong prias is what we get from highly accurate measurements. We can take these parts that are visible and transfer them into virtual reality. But there are parts that are not visible, that we cannot measure while you are wearing a hat set. So whenever information is not present, how can we get the closest to reality? Well, we need some kind of prior in whatever way.