  The lack of transparency of neural networks stays a major break for their
use. The Layerwise Relevance Propagation technique builds heat-maps
representing the relevance of each input in the model s decision. The relevance
spreads backward from the last to the first layer of the Deep Neural Network.
Layer-wise Relevance Propagation does not manage normalization layers, in this
work we suggest a method to include normalization layers. Specifically, we
build an equivalent network fusing normalization layers and convolutional or
fully connected layers. Heatmaps obtained with our method on MNIST and CIFAR 10
datasets are more accurate for convolutional layers. Our study also prevents
from using Layerwise Relevance Propagation with networks including a
combination of connected layers and normalization layer.
