Title: INTERPRETABLE CONVOLUTIONAL FILTER PRUNING. Abstract: The sophisticated structure of Convolutional Neural Network (CNN) allows for
outstanding performance, but at the cost of intensive computation. As significant
redundancies inevitably present in such a structure, many works have been proposed
to prune the convolutional filters for computation cost reduction. Although
extremely effective, most works are based only on quantitative characteristics of
the convolutional filters, and highly overlook the qualitative interpretation of individual
filter’s specific functionality. In this work, we interpreted the functionality
and redundancy of the convolutional filters from different perspectives, and proposed
a functionality-oriented filter pruning method. With extensive experiment
results, we proved the convolutional filters’ qualitative significance regardless of
magnitude, demonstrated significant neural network redundancy due to repetitive
filter functions, and analyzed the filter functionality defection under inappropriate
retraining process. Such an interpretable pruning approach not only offers outstanding
computation cost optimization over previous filter pruning methods, but
also interprets filter pruning process.