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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: VGG16 ×Clear all

11 claims from 7 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

   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.”

3. Computer Science › Advanced Neural Network Applications

   AMC: AutoML for Model Compression and Acceleration on Mobile Devices

   Yihui, Lin, Liu, Wang, Li and Han · arXiv (Cornell University) · 2018

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   1. Unchecked“Under 4x FLOPs reduction, we achieved 2.7% better accuracy than the handcrafted model compression policy for VGG-16 on ImageNet.”
   2. Unchecked“We applied this automated, push-the-button compression pipeline to MobileNet and achieved 1.81x speedup of measured inference latency on an Android phone and 1.43x speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.”

4. Computer Science › Advanced Neural Network Applications

   Channel Pruning for Accelerating Very Deep Neural Networks

   He, Zhang and Sun · arXiv (Cornell University) · 2017

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   1. Unchecked“Our pruned VGG-16 achieves the state-of-the-art results by 5x speed-up along with only 0.3% increase of error.”

5. 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.”

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

   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.”