  Identification of input data points relevant for the classifier (i.e. serve
as the support vector) has recently spurred the interest of researchers for
both interpretability as well as dataset debugging. This paper presents an
in-depth analysis of the methods which attempt to identify the influence of
these data points on the resulting classifier. To quantify the quality of the
influence, we curated a set of experiments where we debugged and pruned the
dataset based on the influence information obtained from different methods. To
do so, we provided the classifier with mislabeled examples that hampered the
overall performance. Since the classifier is a combination of both the data and
the model, therefore, it is essential to also analyze these influences for the
interpretability of deep learning models. Analysis of the results shows that
some interpretability methods can detect mislabels better than using a random
approach, however, contrary to the claim of these methods, the sample selection
based on the training loss showed a superior performance.
