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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: neural network compression ×Clear all

9 claims from 6 papers

1. Computer Science › Advanced Neural Network Applications

   Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

   Han, Mao and Dally · arXiv (Cornell University) · 2015

   The paper introduces 'deep compression', a three-stage pipeline of pruning, trained quantization and Huffman coding that cuts neural network storage by 35x to 49x without affecting accuracy.

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   1. UncheckedThe authors report shrinking the VGG-16 image-recognition network 49-fold, from 552MB to 11.3MB, with no loss of accuracy on ImageNet.“Our method reduced the size of VGG-16 by 49x from 552MB to 11.3MB, again with no loss of accuracy.”
   2. Unchecked“Benchmarked on CPU, GPU and mobile GPU, compressed network has 3x to 4x layerwise speedup and 3x to 7x better energy efficiency.”

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

   Picking Winning Tickets Before Training by Preserving Gradient Flow

   Wang, Zhang and Grosse · arXiv (Cornell University) · 2020

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   1. Unchecked“Our method can prune 80% of the weights of a VGG-16 network on ImageNet at initialization, with only a 1.6% drop in top-1 accuracy.”
   2. Unchecked“Moreover, our method achieves significantly better performance than the baseline at extreme sparsity levels.”

4. 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.”

5. Computer Science › Advanced Neural Network Applications

   Rethinking Weight Decay for Efficient Neural Network Pruning

   Tessier, Gripon, Léonardon, Arzel, Hannagan and Bertrand · Journal of Imaging · 2022

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   1. Unchecked“We show that SWD compares favorably to state-of-the-art approaches, in terms of performance-to-parameters ratio, on the CIFAR-10, Cora, and ImageNet ILSVRC2012 datasets.”

6. 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.”