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arXiv · 1906.05419

Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation

Erik Englesson·Hossein Azizpour
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Abstract

In this work we aim to obtain computationally-efficient uncertainty estimates with deep networks. For this, we propose a modified knowledge distillation procedure that achieves state-of-the-art uncertainty estimates both for in and out-of-distribution samples. Our contributions include a) demonstrating and adapting to distillation's regularization effect b) proposing a novel target teacher distribution c) a simple augmentation procedure to improve out-of-distribution uncertainty estimates d) shedding light on the distillation procedure through comprehensive set of experiments.

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BibTeXRIS

Erik Englesson, Hossein Azizpour. 2019-06-12. Efficient Evaluation-Time Uncertainty Estimation by Improved Distillation. https://arxiv.org/abs/1906.05419

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