  Filter pruning is effective to reduce the computational costs of neural
networks. Existing methods show that updating the previous pruned filter would
enable large model capacity and achieve better performance. However, during the
iterative pruning process, even if the network weights are updated to new
values, the pruning criterion remains the same. In addition, when evaluating
the filter importance, only the magnitude information of the filters is
considered. However, in neural networks, filters do not work individually, but
they would affect other filters. As a result, the magnitude information of each
filter, which merely reflects the information of an individual filter itself,
is not enough to judge the filter importance. To solve the above problems, we
propose Meta-attribute-based Filter Pruning (MFP). First, to expand the
existing magnitude information based pruning criteria, we introduce a new set
of criteria to consider the geometric distance of filters. Additionally, to
explicitly assess the current state of the network, we adaptively select the
most suitable criteria for pruning via a meta-attribute, a property of the
neural network at the current state. Experiments on two image classification
benchmarks validate our method. For ResNet-50 on ILSVRC-2012, we could reduce
more than 50% FLOPs with only 0.44% top-5 accuracy loss.
