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1,212 claims from 763 papers are on the record. 44 have been checked so far; the other 1,168 have no check with a result yet.

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Keyword: ResNet50 ×Clear all

9 claims from 7 papers

1. Computer Science › Advanced Neural Network Applications

   The State of Sparsity in Deep Neural Networks

   Trevor, Elsen and Hooker · arXiv (Cornell University) · 2019

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   1. Unchecked“Across thousands of experiments, we demonstrate that complex techniques (Molchanov et al., 2017; Louizos et al., 2017b) shown to yield high compression rates on smaller datasets perform inconsistently, and that simple magnitude pruning approaches achieve com…

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 Threshold Weight Reparameterization for Learnable Sparsity

   Kusupati, Ramanujan, Somani et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“Notably, STR boosts the accuracy over existing results by up to 10% in the ultra sparse (99%) regime and can also be used to induce low-rank (structured sparsity) in RNNs.”

5. Computer Science › Advanced Neural Network Applications

   Automatic Network Pruning via Hilbert-Schmidt Independence Criterion Lasso under Information Bottleneck Principle

   Guo, Zhang, Zheng et al. · IEEE/CVF International Conference on Computer Vision (ICCV) · 2023

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   1. Unchecked“With ResNet-50, we achieve a 56%-FLOPs reduction by removing 50% of the parameters, with a small loss of 0.08% in the top-1 accuracy on ImageNet.”
   2. Unchecked“For example, with VGG-16, we achieve a 60%-FLOPs reduction by removing 76% of the parameters, with an improvement of 0.40% in top-1 accuracy on CIFAR-10.”

6. Computer Science › Advanced Neural Network Applications

   Towards compressed and efficient CNN architectures via pruning

   Narkhede, Mahajan, Bartakke and Sutaone · Discover Computing · 2024

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   1. Unchecked“For Intel image, CIFAR10 and CIFAR100 datasets the proposed pruning method has compressed AlexNet by 83.2%, 87.19%, and 79.7%, VGG-16 by 83.7%, 85.11%, and 84.06% and ResNet-50 by 62.99%, 62.3% and 58.34% respectively.”

7. 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.”