  Based on filter magnitude ranking (e.g. L1 norm), conventional filter pruning
methods for Convolutional Neural Networks (CNNs) have been proved with great
effectiveness in computation load reduction. Although effective, these methods
are rarely analyzed in a perspective of filter functionality. In this work, we
explore the filter pruning and the retraining through qualitative filter
functionality interpretation. We find that the filter magnitude based method
fails to eliminate the filters with repetitive functionality. And the
retraining phase is actually used to reconstruct the remained filters for
functionality compensation for the wrongly-pruned critical filters. With a
proposed functionality-oriented pruning method, we further testify that, by
precisely addressing the filter functionality redundancy, a CNN can be pruned
without considerable accuracy drop, and the retraining phase is unnecessary.
