Pruning-aware Sparse Regularization for Network Pruning

• URL: http://arxiv.org/abs/2201.06776v2
• Date: Fri, 20 Oct 2023 13:10:33 GMT
• Title: Pruning-aware Sparse Regularization for Network Pruning
• Authors: Nanfei Jiang, Xu Zhao, Chaoyang Zhao, Yongqi An, Ming Tang, Jinqiao Wang
• Abstract summary: Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. We propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization.
• Score: 38.13617051756663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Structural neural network pruning aims to remove the redundant channels in the deep convolutional neural networks (CNNs) by pruning the filters of less importance to the final output accuracy. To reduce the degradation of performance after pruning, many methods utilize the loss with sparse regularization to produce structured sparsity. In this paper, we analyze these sparsity-training-based methods and find that the regularization of unpruned channels is unnecessary. Moreover, it restricts the network's capacity, which leads to under-fitting. To solve this problem, we propose a novel pruning method, named MaskSparsity, with pruning-aware sparse regularization. MaskSparsity imposes the fine-grained sparse regularization on the specific filters selected by a pruning mask, rather than all the filters of the model. Before the fine-grained sparse regularization of MaskSparity, we can use many methods to get the pruning mask, such as running the global sparse regularization. MaskSparsity achieves 63.03%-FLOPs reduction on ResNet-110 by removing 60.34% of the parameters, with no top-1 accuracy loss on CIFAR-10. On ILSVRC-2012, MaskSparsity reduces more than 51.07% FLOPs on ResNet-50, with only a loss of 0.76% in the top-1 accuracy. The code is released at https://github.com/CASIA-IVA-Lab/MaskSparsity. Moreover, we have integrated the code of MaskSparity into a PyTorch pruning toolkit, EasyPruner, at https://gitee.com/casia_iva_engineer/easypruner.

Related papers

• Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter Sampler [103.97487121678276]
  Filter pruning simultaneously accelerates the computation and reduces the memory overhead of CNNs. We propose a novel Knowledge-driven Differential Filter Sampler(KDFS) with Masked Filter Modeling(MFM) framework for filter pruning.
  arXiv  Detail & Related papers  (2023-07-01T02:28:41Z)
• End-to-End Sensitivity-Based Filter Pruning [49.61707925611295]
  We present a sensitivity-based filter pruning algorithm (SbF-Pruner) to learn the importance scores of filters of each layer end-to-end. Our method learns the scores from the filter weights, enabling it to account for the correlations between the filters of each layer.
  arXiv  Detail & Related papers  (2022-04-15T10:21:05Z)
• Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters [151.2423480789271]
  A novel pruning method, termed CLR-RNF, is proposed for filter-level network pruning. We conduct image classification on CIFAR-10 and ImageNet to demonstrate the superiority of our CLR-RNF over the state-of-the-arts.
  arXiv  Detail & Related papers  (2022-02-15T04:53:24Z)
• Non-Parametric Adaptive Network Pruning [125.4414216272874]
  We introduce non-parametric modeling to simplify the algorithm design. Inspired by the face recognition community, we use a message passing algorithm to obtain an adaptive number of exemplars. EPruner breaks the dependency on the training data in determining the "important" filters.
  arXiv  Detail & Related papers  (2021-01-20T06:18:38Z)
• Filter Pruning using Hierarchical Group Sparse Regularization for Deep Convolutional Neural Networks [3.5636461829966093]
  We propose a filter pruning method using the hierarchical group sparse regularization. It can reduce more than 50% parameters of ResNet for CIFAR-10 with only 0.3% decrease in the accuracy of test samples. Also, 34% parameters of ResNet are reduced for TinyImageNet-200 with higher accuracy than the baseline network.
  arXiv  Detail & Related papers  (2020-11-04T16:29:41Z)
• Filter Sketch for Network Pruning [184.41079868885265]
  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.
  arXiv  Detail & Related papers  (2020-01-23T13:57:08Z)
• Pruning CNN's with linear filter ensembles [0.0]
  We use pruning to reduce the network size and -- implicitly -- the number of floating point operations (FLOPs) We develop a novel filter importance norm that is based on the change in the empirical loss caused by the presence or removal of a component from the network architecture. We evaluate our method on a fully connected network, as well as on the ResNet architecture trained on the CIFAR-10 dataset.
  arXiv  Detail & Related papers  (2020-01-22T16:52:06Z)
• Campfire: Compressible, Regularization-Free, Structured Sparse Training for Hardware Accelerators [0.04666493857924356]
  This paper studies structured sparse training of CNNs with a gradual pruning technique. We simplify the structure of the enforced sparsity so that it reduces overhead caused by regularization. We show that our method creates a sparse version of ResNet-50 and ResNet-50 v1.5 on full ImageNet while remaining within a negligible 1% margin of accuracy loss.
  arXiv  Detail & Related papers  (2020-01-09T23:15:43Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.