Layerwise Relevance Propagation

• It relies on a conservation principle to propagate the outcome decision back without using gradients. The idea behind it is a decomposition of prediction function as a sum of layerwise relevance values. When LRP is applied to deep ReLU networks, LRP can be understood as a deep Taylor decomposition of the prediction. This principle ensures that the prediction activity is fully redistributed through all the layers onto the input variables
• suffers from the shattered gradients problem