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1,167 claims from 736 papers are on the record. 43 have been checked so far; the other 1,124 have no check with a result yet.

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Status: Unchecked ×Keyword: filter pruning ×Clear all

18 claims from 12 papers

1. Computer Science › Advanced Neural Network Applications

   Pruning Filters for Efficient ConvNets

   Li, Kadav, Đurđanović, Samet and Graf · arXiv (Cornell University) · 2016

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   1. Unchecked“In contrast to pruning weights, this approach does not result in sparse connectivity patterns.”
   2. Unchecked“We show that even simple filter pruning techniques can reduce inference costs for VGG-16 by up to 34% and ResNet-110 by up to 38% on CIFAR10 while regaining close to the original accuracy by retraining the networks.”

2. Computer Science › Advanced Neural Network Applications

   Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration

   He, Ding, Liu, Zhu, Zhang and Yang · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2020

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   1. Unchecked“Notably, on ILSVRC-2012, our LFPC reduces more than 60% FLOPs on ResNet-50 with only 0.83% top-5 accuracy loss.”

3. Computer Science › Advanced Neural Network Applications

   ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

   Luo, Wu and Lin · arXiv (Cornell University) · 2017

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   1. Unchecked“Similar experiments with ResNet-50 reveal that even for a compact network, ThiNet can also reduce more than half of the parameters and FLOPs, at the cost of roughly 1$\%$ top-5 accuracy drop.”
   2. Unchecked“We formally establish filter pruning as an optimization problem, and reveal that we need to prune filters based on statistics information computed from its next layer, not the current layer, which differentiates ThiNet from existing methods.”

4. Computer Science › Advanced Neural Network Applications

   Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

   He, Kang, Dong, Fu and Yang · arXiv (Cornell University) · 2018

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   1. Unchecked“Empirically, SFP from scratch outperforms the previous filter pruning methods.”
   2. Unchecked“Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art.”

5. Computer Science › Advanced Neural Network Applications

   Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration

   He, Liu, Wang, Hu and Yang · arXiv (Cornell University) · 2018

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   1. Unchecked“Notably, on CIFAR-10, FPGM reduces more than 52% FLOPs on ResNet-110 with even 2.69% relative accuracy improvement.”
   2. Unchecked“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.”

6. Computer Science › Advanced Neural Network Applications

   Deep Model Compression based on the Training History

   Basha, Farazuddin, Viswanath, Dubey and Mukherjee · Neurocomputing · 2024

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   1. Unchecked“The proposed pruning method outperforms the state-of-the-art in terms of FLOPs reduction (floating-point operations) by 97.98%, 83.42%, 78.43%, 74.95%, and 75.45% for LeNet-5, VGG-16, ResNet-56, ResNet-110, and ResNet-50, respectively, while maintaining the…

7. Computer Science › Advanced Neural Network Applications

   Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

   Li, Gu, Christoph, Van Gool and Timofte · Lirias · 2020

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   1. Unchecked“For example, in popular network architectures with shortcut connections (e.g. ResNet), filter pruning cannot deal with the last convolutional layer in a ResBlock while the low-rank decomposition methods can.”

8. Computer Science › Advanced Neural Network Applications

   Convolutional Neural Network Pruning with Structural Redundancy Reduction

   Wang, Li and Wang · arXiv (Cornell University) · 2021

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   1. Unchecked“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.”

9. Computer Science › Advanced Neural Network Applications

   HRank: Filter Pruning using High-Rank Feature Map

   Lin, Ji, Wang et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“Our HRank is inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.”
   2. Unchecked“For example, with ResNet-110, we achieve a 58.2%-FLOPs reduction by removing 59.2% of the parameters, with only a small loss of 0.14% in top-1 accuracy on CIFAR-10.”
   3. Unchecked“With Res-50, we achieve a 43.8%-FLOPs reduction by removing 36.7% of the parameters, with only a loss of 1.17% in the top-1 accuracy on ImageNet.”

10. Computer Science › Advanced Neural Network Applications

    Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning

    Lin, Ji, Li, Deng and Li · arXiv (Cornell University) · 2019

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    1. Unchecked“AULM follows the principle of ADMM and alternates between promoting the structured sparsity of CNNs and optimizing the recognition loss, which leads to a very efficient solver (2.5x to the most recent work that directly solves the group sparsity-based regula…

11. Computer Science › Advanced Neural Network Applications

    Training Compact CNNs for Image Classification using Dynamic-coded Filter Fusion

    Lin, Chen, Chao and Ji · arXiv (Cornell University) · 2021

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    1. Unchecked“For example, our DCFF derives a compact VGGNet-16 with only 72.77M FLOPs and 1.06M parameters while reaching top-1 accuracy of 93.47% on CIFAR-10.”

12. Computer Science › Advanced Neural Network Applications

    ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting

    Ding, Hao, Tan et al. · arXiv (Cornell University) · 2020

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    1. Unchecked“ResRep slims down a standard ResNet-50 with 76.15% accuracy on ImageNet to a narrower one with only 45% FLOPs and no accuracy drop, which is the first to achieve lossless pruning with such a high compression ratio.”