Simple techniques work surprisingly well for neural network test prioritization and active learning (replicability study)

ry fast and simple TIP, and show that it outperforms\nmore elaborate techniques such as neuron- and surprise coverage. In a\nlarge-scale study (4 case studies, 8 test datasets, 32'200 trained models) we\nverify their findings. However, we also find that other comparable or even\nsimpler baselines from the field of uncertainty quantification, such as the\npredicted softmax likelihood or the entropy of the predicted softmax\nlikelihoods perform equally well as DeepGini.\n