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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: channel pruning ×Clear all

13 claims from 9 papers

1. 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.”

2. 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.”

3. 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.”

4. Computer Science › Advanced Neural Network Applications

   MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning

   Liu, Mu, Zhang et al. · arXiv (Cornell University) · 2019

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   1. Unchecked“Compared to the state-of-the-art pruning methods, we have demonstrated superior performances on MobileNet V1/V2 and ResNet.”
   2. Unchecked“The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time.”

5. Computer Science › Advanced Neural Network Applications

   Structured Pruning for Efficient Convolutional Neural Networks via Incremental Regularization

   Wang, Hu, Zhang, Wang, Yu and Hu · IEEE Journal of Selected Topics in Signal Processing · 2019

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   1. Unchecked“Further extensive experiments with popular CNNs on CIFAR-10 and ImageNet datasets show that IncReg achieves comparable to even better results compared with state-of-the-arts.”

6. Computer Science › Advanced Neural Network Applications

   Exploiting Channel Similarity for Network Pruning

   Zhao, Zhang and Ni · IEEE Transactions on Circuits and Systems for Video Technology · 2023

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   1. Unchecked“Precisely, we argue that channels revealing similar feature information have functional overlap and that each such similarity group can be reduced to a few representatives with little impact on the representational power of the model.”
   2. Unchecked“On ImageNet, our pruned ResNet-50 with 30% FLOPs reduced outperforms the original model.”

7. Computer Science › Advanced Neural Network Applications

   Channel Pruning via Lookahead Search Guided Reinforcement Learning

   Wang and Li · IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · 2022

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   1. Unchecked“Experiments on MNIST, CIFAR-10, and ILSVRC-2012 validate the effectiveness of our approach compared to both traditional and automated existing channel pruning approaches.”

8. 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.”

9. Computer Science › Advanced Neural Network Applications

   PSE-Net: Channel Pruning for Convolutional Neural Networks with Parallel-subnets Estimator

   Wang, Xie, Liu, Zhang and Cheng · arXiv (Cornell University) · 2024

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   1. Unchecked“For example, under 300M FLOPs constraint, our pruned MobileNetV2 achieves 75.2% Top-1 accuracy on ImageNet dataset, exceeding the original MobileNetV2 by 2.6 units while only cost 30%/16% times than BCNet/AutoAlim.”