  Popular network pruning algorithms reduce redundant information by optimizing
hand-crafted models, and may cause suboptimal performance and long time in
selecting filters. We innovatively introduce adaptive exemplar filters to
simplify the algorithm design, resulting in an automatic and efficient pruning
approach called EPruner. Inspired by the face recognition community, we use a
message passing algorithm Affinity Propagation on the weight matrices to obtain
an adaptive number of exemplars, which then act as the preserved filters.
EPruner breaks the dependency on the training data in determining the
"important" filters and allows the CPU implementation in seconds, an order of
magnitude faster than GPU based SOTAs. Moreover, we show that the weights of
exemplars provide a better initialization for the fine-tuning. On VGGNet-16,
EPruner achieves a 76.34%-FLOPs reduction by removing 88.80% parameters, with
0.06% accuracy improvement on CIFAR-10. In ResNet-152, EPruner achieves a
65.12%-FLOPs reduction by removing 64.18% parameters, with only 0.71% top-5
accuracy loss on ILSVRC-2012. Our code can be available at
https://github.com/lmbxmu/EPruner.
