  Test Input Prioritizers (TIP) for Deep Neural Networks (DNN) are an important
technique to handle the typically very large test datasets efficiently, saving
computation and labeling costs. This is particularly true for large-scale,
deployed systems, where inputs observed in production are recorded to serve as
potential test or training data for the next versions of the system. Feng et.
al. propose DeepGini, a very fast and simple TIP, and show that it outperforms
more elaborate techniques such as neuron- and surprise coverage. In a
large-scale study (4 case studies, 8 test datasets, 32'200 trained models) we
verify their findings. However, we also find that other comparable or even
simpler baselines from the field of uncertainty quantification, such as the
predicted softmax likelihood or the entropy of the predicted softmax
likelihoods perform equally well as DeepGini.
