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Neural networks have been shown vulnerable to a variety of adversarialalgorithms. A crucial step to understanding the rationale for this lack ofrobustness is to assess the potential of the neural networks' representation toencode the existing features. Here, we propose a method to understand therepresentation quality of the neural networks using a novel test based onZero-Shot Learning, entitled Raw Zero-Shot. The principal idea is that, if analgorithm learns rich features, such features should be able to interpret"unknown" classes as an aggregate of previously learned features. This isbecause unknown classes usually share several regular features with recognisedclasses, given the features learned are general enough. We further introducetwo metrics to assess these learned features to interpret unknown classes. Oneis based on inter-cluster validation technique (Davies-Bouldin Index), and theother is based on the distance to an approximated ground-truth. Experimentssuggest that adversarial defences improve the representation of theclassifiers, further suggesting that to improve the robustness of theclassifiers, one has to improve the representation quality also. Experimentsalso reveal a strong association (a high Pearson Correlation and low p-value)between the metrics and adversarial attacks. Interestingly, the resultsindicate that dynamic routing networks such as CapsNet have betterrepresentation while current deeper neural networks are trading offrepresentation quality for accuracy.  Code available at http://bit.ly/RepresentationMetrics.

Representation Quality Of Neural Networks Links To Adversarial Attacks and Defences