  Neural networks have been shown vulnerable to a variety of adversarial
algorithms. A crucial step to understanding the rationale for this lack of
robustness is to assess the potential of the neural networks' representation to
encode the existing features. Here, we propose a method to understand the
representation quality of the neural networks using a novel test based on
Zero-Shot Learning, entitled Raw Zero-Shot. The principal idea is that, if an
algorithm learns rich features, such features should be able to interpret
"unknown" classes as an aggregate of previously learned features. This is
because unknown classes usually share several regular features with recognised
classes, given the features learned are general enough. We further introduce
two metrics to assess these learned features to interpret unknown classes. One
is based on inter-cluster validation technique (Davies-Bouldin Index), and the
other is based on the distance to an approximated ground-truth. Experiments
suggest that adversarial defences improve the representation of the
classifiers, further suggesting that to improve the robustness of the
classifiers, one has to improve the representation quality also. Experiments
also reveal a strong association (a high Pearson Correlation and low p-value)
between the metrics and adversarial attacks. Interestingly, the results
indicate that dynamic routing networks such as CapsNet have better
representation while current deeper neural networks are trading off
representation quality for accuracy.
  Code available at http://bit.ly/RepresentationMetrics.
