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Their network’s performance was on par with earlier state-of-the-art models, but it surely also gained the power to estimate its own uncertainty. As the researchers had hoped, the network projected excessive uncertainty for pixels the place it predicted the incorrect depth. It was very calibrated to the errors that the community makes, which we believe was probably the most important issues in judging the quality of a brand new uncertainty estimator,â€ Amini says.
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