  While rule-based attribution methods have proven useful for providing local
explanations for Deep Neural Networks, explaining modern and more varied
network architectures yields new challenges in generating trustworthy
explanations, since the established rule sets might not be sufficient or
applicable to novel network structures. As an elegant solution to the above
issue, network canonization has recently been introduced. This procedure
leverages the implementation-dependency of rule-based attributions and
restructures a model into a functionally identical equivalent of alternative
design to which established attribution rules can be applied. However, the idea
of canonization and its usefulness have so far only been explored
qualitatively. In this work, we quantitatively verify the beneficial effects of
network canonization to rule-based attributions on VGG-16 and ResNet18 models
with BatchNorm layers and thus extend the current best practices for obtaining
reliable neural network explanations.
