Filter Pruning via Geometric Medianfor Deep Convolutional Neural Networks Acceleration
Abstract
Previous works utilized “smaller-norm-less-important”
criterion to prune filters with smaller norm values in a convolutional neural network. In this paper, we analyze this
norm-based criterion and point out that its effectiveness depends on two requirements that are not always met: (1)
the norm deviation of the filters should be large; (2) the
minimum norm of the filters should be small. To solve
this problem, we propose a novel filter pruning method,
namely Filter Pruning via Geometric Median (FPGM), to
compress the model regardless of those two requirements.
Unlike previous methods, FPGM compresses CNN models
by pruning filters with redundancy, rather than those with
“relatively less” importance. When applied to two image
classification benchmarks, our method validates its usefulness and strengths. Notably, on CIFAR-10, FPGM reduces
more than 52% FLOPs on ResNet-110 with even 2.69%
relative accuracy improvement. Moreover, on ILSVRC-
2012, FPGM reduces more than 42% FLOPs on ResNet-
101 without top-5 accuracy drop, which has advanced the
state-of-the-art. Code is publicly available on GitHub:
https://github.com/he-y/filter-pruning-geometric-median