Classification Uncertainty of Deep Neural Networks Based on Gradient Information
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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 (E)MNIST data set that for several such metrics we achieve the same meta classification accuracy -- i.e. the task of classifying correctly predicted labels as correct and incorrectly predicted ones as incorrect without knowing the actual label -- as for entropy thresholding. Meta classification rates for out of sample images can be increased when using entropy together with several gradient based metrics as input quantities for a meta-classifier. This proves that our gradient based metrics do not contain the same information as the entropy. We also apply meta classification to concepts not used during training: EMNIST/Omniglot letters, CIFAR10 and noise. Meta classifiers only trained on the uncertainty metrics of classes available during training usually do not perform equally well for all the unknown concepts letters, CIFAR10 and uniform noise. If we however allow the meta classifier to be trained on uncertainty metrics including some samples of some or all of the categories, meta classification for concepts remote from MNIST digits can be improved considerably.
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