Convolutional Neural Network Pruning with Structural Redundancy Reduction

• URL: http://arxiv.org/abs/2104.03438v1
• Date: Thu, 8 Apr 2021 00:16:24 GMT
• Title: Convolutional Neural Network Pruning with Structural Redundancy Reduction
• Authors: Zi Wang, Chengcheng Li, Xiangyang Wang
• Abstract summary: We claim that identifying structural redundancy plays a more essential role than finding unimportant filters. We propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy.
• Score: 11.381864384054824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Convolutional neural network (CNN) pruning has become one of the most successful network compression approaches in recent years. Existing works on network pruning usually focus on removing the least important filters in the network to achieve compact architectures. In this study, we claim that identifying structural redundancy plays a more essential role than finding unimportant filters, theoretically and empirically. We first statistically model the network pruning problem in a redundancy reduction perspective and find that pruning in the layer(s) with the most structural redundancy outperforms pruning the least important filters across all layers. Based on this finding, we then propose a network pruning approach that identifies structural redundancy of a CNN and prunes filters in the selected layer(s) with the most redundancy. Experiments on various benchmark network architectures and datasets show that our proposed approach significantly outperforms the previous state-of-the-art.

Related papers

• Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration [62.41329042683779]
  We propose a high-accuracy rotation equivariant proximal network that embeds rotation symmetry priors into the deep unfolding framework. This study makes efforts to suggest a high-accuracy rotation equivariant proximal network that effectively embeds rotation symmetry priors into the deep unfolding framework.
  arXiv  Detail & Related papers  (2023-12-25T11:53:06Z)
• Network Pruning Spaces [12.692532576302426]
  Network pruning techniques, including weight pruning and filter pruning, reveal that most state-of-the-art neural networks can be accelerated without a significant performance drop. This work focuses on filter pruning which enables accelerated inference with any off-the-shelf deep learning library and hardware.
  arXiv  Detail & Related papers  (2023-04-19T06:52:05Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• RDRN: Recursively Defined Residual Network for Image Super-Resolution [58.64907136562178]
  Deep convolutional neural networks (CNNs) have obtained remarkable performance in single image super-resolution. We propose a novel network architecture which utilizes attention blocks efficiently.
  arXiv  Detail & Related papers  (2022-11-17T11:06:29Z)
• Pushing the Efficiency Limit Using Structured Sparse Convolutions [82.31130122200578]
  We propose Structured Sparse Convolution (SSC), which leverages the inherent structure in images to reduce the parameters in the convolutional filter. We show that SSC is a generalization of commonly used layers (depthwise, groupwise and pointwise convolution) in efficient architectures'' Architectures based on SSC achieve state-of-the-art performance compared to baselines on CIFAR-10, CIFAR-100, Tiny-ImageNet, and ImageNet classification benchmarks.
  arXiv  Detail & Related papers  (2022-10-23T18:37:22Z)
• Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction [4.9613162734482215]
  This paper presents a novel training scheme based on composite constraints that prune redundant filters and minimize their effect on overall network learning via sparsity promotion. Our tests on several pixel-wise segmentation benchmarks show that the number of neurons and the memory footprint of networks in the test phase are significantly reduced without affecting performance.
  arXiv  Detail & Related papers  (2022-10-14T01:34:49Z)
• Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning [41.19516938181544]
  Current network compression methods have two open problems: first, there lacks a theoretical framework to estimate the maximum compression rate; second, some layers may get over-prunned, resulting in significant network performance drop. This study propose a gradient-matrix singularity analysis-based method to estimate the maximum network redundancy. Guided by that maximum rate, a novel and efficient hierarchical network pruning algorithm is developed to maximally condense the neuronal network structure without sacrificing network performance.
  arXiv  Detail & Related papers  (2022-06-07T21:30:47Z)
• Manifold Regularized Dynamic Network Pruning [102.24146031250034]
  This paper proposes a new paradigm that dynamically removes redundant filters by embedding the manifold information of all instances into the space of pruned networks. The effectiveness of the proposed method is verified on several benchmarks, which shows better performance in terms of both accuracy and computational cost.
  arXiv  Detail & Related papers  (2021-03-10T03:59:03Z)
• Grow-Push-Prune: aligning deep discriminants for effective structural network compression [5.532477732693]
  This paper attempts to derive task-dependent compact models from a deep discriminant analysis perspective. We propose an iterative and proactive approach for classification tasks which alternates between a pushing step and a pruning step. Experiments on the MNIST, CIFAR10, and ImageNet datasets demonstrate our approach's efficacy.
  arXiv  Detail & Related papers  (2020-09-29T01:29:23Z)
• A "Network Pruning Network" Approach to Deep Model Compression [62.68120664998911]
  We present a filter pruning approach for deep model compression using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target network. The compressed model produced by our approach is generic and does not need any special hardware/software support.
  arXiv  Detail & Related papers  (2020-01-15T20:38:23Z)

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.