  Deep neural networks tend to make overconfident predictions and often require
additional detectors for misclassifications, particularly for safety-critical
applications. Existing detection methods usually only focus on adversarial
attacks or out-of-distribution samples as reasons for false predictions.
However, generalization errors occur due to diverse reasons often related to
poorly learning relevant invariances. We therefore propose GIT, a holistic
approach for the detection of generalization errors that combines the usage of
gradient information and invariance transformations. The invariance
transformations are designed to shift misclassified samples back into the
generalization area of the neural network, while the gradient information
measures the contradiction between the initial prediction and the corresponding
inherent computations of the neural network using the transformed sample. Our
experiments demonstrate the superior performance of GIT compared to the
state-of-the-art on a variety of network architectures, problem setups and
perturbation types.
