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.
authors: Zhuwei Qin Fuxun Yu Chenchen Liu Xiang Chen
hIndex: Fuxun Yu 11