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Adaptive Temperature Scaling for Robust Calibration of Deep Neural Networks
We show that when there is plenty of data complex models like neural networks yield better performance, but are prone to fail when the amount of data is limited, a common situation in certain post-hoc calibration applications like medical diagnosis.
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