Breaking Batch Normalization for better explainability of Deep Neural Networks through Layer-wise Relevance Propagation

Understand

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.