  We study the quantification of uncertainty of Convolutional Neural Networks
(CNNs) based on gradient metrics. Unlike the classical softmax entropy, such
metrics gather information from all layers of the CNN. We show for the EMNIST
digits data set that for several such metrics we achieve the same meta
classification accuracy -- i.e. the task of classifying predictions as correct
or incorrect without knowing the actual label -- as for entropy thresholding.
We apply meta classification to unknown concepts (out-of-distribution samples)
-- EMNIST/Omniglot letters, CIFAR10 and noise -- and demonstrate that meta
classification rates for unknown concepts can be increased when using entropy
together with several gradient based metrics as input quantities for a meta
classifier. Meta classifiers only trained on the uncertainty metrics of known
concepts, i.e. EMNIST digits, usually do not perform equally well for all
unknown concepts. If we however allow the meta classifier to be trained on
uncertainty metrics for some out-of-distribution samples, meta classification
for concepts remote from EMNIST digits (then termed known unknowns) can be
improved considerably.
