  State of the art Deep Neural Networks (DNN) can now achieve above human level
accuracy on image classification tasks. However their outstanding performances
come along with a complex inference mechanism making them arduously
interpretable models. In order to understand the underlying prediction rules of
DNNs, Dhamdhere et al. propose an interpretability method to break down a DNN
prediction score as sum of its hidden unit contributions, in the form of a
metric called conductance. Analyzing conductances of DNN hidden units, we find
out there is a difference in how wrong and correct predictions are inferred. We
identify distinguishable patterns of hidden unit activations for wrong and
correct predictions. We then use an error detector in the form of a binary
classifier on top of the DNN to automatically discriminate wrong and correct
predictions of the DNN based on their hidden unit activations. Detected wrong
predictions are discarded, increasing the model robustness. A different
approach to distinguish wrong and correct predictions of DNNs is proposed by
Wang et al. whose method is based on the premise that input samples leading a
DNN into making wrong predictions are less stable to the DNN weight changes
than correctly classified input samples. In our study, we compare both methods
and find out by combining them that better detection of wrong predictions can
be achieved.
