Rate-In adapts dropout rates per layer and per input at inference by measuring mutual-information loss, improving calibration and sharpening uncertainty estimates in medical imaging tests.
The medical segmentation decathlon.Nature communications, 13(1):4128, 2022
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Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation
Rate-In adapts dropout rates per layer and per input at inference by measuring mutual-information loss, improving calibration and sharpening uncertainty estimates in medical imaging tests.