What does a conservative technique mean in the context of neural networks? I am reading this article on Layer-wise relevance propagation method and I can't understand this particular paragraph


  LRP is a conservative technique, meaning the magnitude of any output y is conserved through the backpropagation process and is equal to the sum of the relevance map R of the input layer. This property holds for any consecutive layers j and k, and by transitivity for the input and output layer.


What does this even mean? I can't understand what the author is trying to say. I understand what backpropagation is but not what is being said related to backpropagation!
In this layered graph structure, the
relevance conservation property can be formulated as follows: Let
j and k be indices for neurons of two successive layers. Let Rk be
the relevance of neuron k for the prediction f (x). We define R j←k
as the share of Rk that is redistributed to neuron j in the lower
layer. The conservation property for this neuron imposes
∑j R j←k = Rk.
Likewise, neurons in the lower layer aggregate all relevance coming
from the neurons from the higher layer:
Rj = ∑k R j←k
These two equations, when combined, also ensure a relevance conservation property between layers
Refer to this paper Methods for interpreting and understanding deep neural networks by Grégoire Montavon for more details