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arXiv · 2107.01606

A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification

Geir K. Nilsen·Antonella Z. Munthe-Kaas·Hans J. Skaug·Morten Brun
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Abstract

We validate the recently introduced deep learning classification adapted Delta method by a comparison with the classical Bootstrap. We show that there is a strong linear relationship between the quantified predictive epistemic uncertainty levels obtained from the two methods when applied on two LeNet-based neural network classifiers using the MNIST and CIFAR-10 datasets. Furthermore, we demonstrate that the Delta method offers a five times computation time reduction compared to the Bootstrap.

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Geir K. Nilsen, Antonella Z. Munthe-Kaas, Hans J. Skaug, Morten Brun. 2021-07-04. A Comparison of the Delta Method and the Bootstrap in Deep Learning Classification. https://arxiv.org/abs/2107.01606

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