  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.
