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1,213 claims from 764 papers are on the record. 45 have been checked so far; the other 1,168 have no check with a result yet.

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Status: Unchecked ×Keyword: model compression ×Clear all

17 claims from 11 papers

1. Computer Science › Advanced Neural Network Applications

   Distilling the Knowledge in a Neural Network

   Hinton, Vinyals and Jeff · arXiv (Cornell University) · 2015

   The paper develops a way to compress an ensemble of neural networks into one model, and proposes an ensemble that adds specialist models to full models.

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   1. UncheckedThe paper says its specialist models, unlike a mixture of experts, can be trained rapidly and in parallel.“Unlike a mixture of experts, these specialist models can be trained rapidly and in parallel.”

2. 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…

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

   Discrimination-aware Network Pruning for Deep Model Compression

   Liu, Zhuang, Zhuang et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

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   1. Unchecked“For example, on ILSVRC-12, the resultant ResNet-50 model with 30% reduction of channels even outperforms the baseline model by 0.36% in terms of Top-1 accuracy.”
   2. Unchecked“The pruned MobileNetV1 and MobileNetV2 achieve 1.93x and 1.42x inference acceleration on a mobile device, respectively, with negligible performance degradation.”

5. Computer Science › Advanced Neural Network Applications

   Network Pruning via Performance Maximization

   Gao, Huang, Cai and Huang · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · 2021

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   1. Unchecked“Extensive experimental results show that the proposed method can achieve state-of-the-art performance with ResNet, MobileNetV2, and ShuffleNetV2+ on ImageNet and CIFAR-10.”

6. Computer Science › Advanced Neural Network Applications

   NISP: Pruning Networks using Neuron Importance Score Propagation

   Yu, Li, Chen et al. · arXiv (Cornell University) · 2017

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   1. Unchecked“In contrast, we argue that it is essential to prune neurons in the entire neuron network jointly based on a unified goal: minimizing the reconstruction error of important responses in the "final response layer" (FRL), which is the second-to-last layer before…
   2. Unchecked“Specifically, we apply feature ranking techniques to measure the importance of each neuron in the FRL, and formulate network pruning as a binary integer optimization problem and derive a closed-form solution to it for pruning neurons in earlier layers.”

7. 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.”

8. Computer Science › Advanced Neural Network Applications

   End-to-End Supermask Pruning: Learning to Prune Image Captioning Models

   Tan, Chan and Chuah · Pattern Recognition · 2021

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   1. Unchecked“Empirically, we show that an 80% to 95% sparse network (up to 75% reduction in model size) can either match or outperform its dense counterpart.”

9. 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.”

10. Computer Science › Advanced Neural Network Applications

    PAMS: Quantized Super-Resolution via Parameterized Max Scale

    Li, Yan, Lin et al. · arXiv (Cornell University) · 2020

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    1. Unchecked“Extensive experiments demonstrate that the proposed PAMS scheme can well compress and accelerate the existing SR models such as EDSR and RDN.”
    2. Unchecked“Notably, 8-bit PAMS-EDSR improves PSNR on Set5 benchmark from 32.095dB to 32.124dB with 2.42$\times$ compression ratio, which achieves a new state-of-the-art.”

11. Computer Science › Advanced Neural Network Applications

    GAT TransPruning: progressive channel pruning strategy combining graph attention network and transformer

    Lin, Wang and Lin · PeerJ Computer Science · 2024

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    1. Unchecked“The experimental results reveal that the accuracy rate only drops by 6.58% when the channel pruning rate is 89% for VGG-19/CIFAR-100.”
    2. Unchecked“In addition, the lightweight model inference speed is 9.10 times faster than that of the original large model.”