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Article overview
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Pruning Filter via Geometric Median for Deep Convolutional Neural Networks Acceleration | | Yang He
; Ping Liu
; Ziwei Wang
; Yi Yang
; | | Date: |
1 Nov 2018 | | 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 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, PFGM
compresses CNN models by determining and pruning those filters with redundant
information via Geometric Median (GM), 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, PFGM reduces more
than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement.
Besides, on ILSCRC-2012, PFGM reduces more than 42% FLOPs on ResNet-101 without
top-5 accuracy drop, which has advanced the state-of-the-art. | | Source: | arXiv, 1811.0250 | | Services: | Forum | Review | PDF | Favorites | |
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