Filter Sketch for Network Pruning

• URL: http://arxiv.org/abs/2001.08514v4
• Date: Tue, 25 May 2021 03:12:53 GMT
• Title: Filter Sketch for Network Pruning
• Authors: Mingbao Lin, Liujuan Cao, Shaojie Li, Qixiang Ye, Yonghong Tian, Jianzhuang Liu, Qi Tian, Rongrong Ji
• Abstract summary: We propose a novel network pruning approach by information preserving of pre-trained network weights (filters) Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of FLOPs and prunes 59.9% of network parameters with negligible accuracy cost.
• Score: 184.41079868885265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel network pruning approach by information preserving of pre-trained network weights (filters). Network pruning with the information preserving is formulated as a matrix sketch problem, which is efficiently solved by the off-the-shelf Frequent Direction method. Our approach, referred to as FilterSketch, encodes the second-order information of pre-trained weights, which enables the representation capacity of pruned networks to be recovered with a simple fine-tuning procedure. FilterSketch requires neither training from scratch nor data-driven iterative optimization, leading to a several-orders-of-magnitude reduction of time cost in the optimization of pruning. Experiments on CIFAR-10 show that FilterSketch reduces 63.3% of FLOPs and prunes 59.9% of network parameters with negligible accuracy cost for ResNet-110. On ILSVRC-2012, it reduces 45.5% of FLOPs and removes 43.0% of parameters with only 0.69% accuracy drop for ResNet-50. Our code and pruned models can be found at https://github.com/lmbxmu/FilterSketch.

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