  Convolutional neural networks (CNN) are generally designed with a heuristic
initialization of network architecture and trained for a certain task. This
often leads to overparametrization after learning and induces redundancy in the
information flow paths within the network. This robustness and reliability is
at the increased cost of redundant computations. Several methods have been
proposed which leverage metrics that quantify the redundancy in each layer.
However, layer-wise evaluation in these methods disregards the long-range
redundancy which exists across depth on account of the distributed nature of
the features learned by the model. In this paper, we propose (i) a mechanism to
empirically demonstrate the robustness in performance of a CNN on account of
redundancy across its depth, (ii) a method to identify the systemic redundancy
in response of a CNN across depth using the understanding of unit impulse
response, we subsequently demonstrate use of these methods to interpret
redundancy in few networks as example. These techniques provide better insights
into the internal dynamics of a CNN
