"Reliable classification under distribution shift is still out of reach for current machine learning ... robustness improvements on real data may require new methodology" - ICLR 2020 paper under review, evaluating 147 ImageNet models. https://openreview.net/pdf?id=HyxPIyrFvH … Time for a Reboot?
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The issue of generalization is less acute for Google and others coz they truly never encounter a new image which isn't close enough to a sample in their training dataset. E.g. AR view in maps. They have collected street views for years now in all weather conditions.
they (Waymo) still can't make their driverless cars safe enough for the real world
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That's a special usecase with extremely high cost for making any mistake. Most CV usecases don't have such stringent success criteria.
Thanks. Twitter will use this to make your timeline better. UndoUndo
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