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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: FLOPs reduction ×Clear all

14 claims from 8 papers

1. 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.”

2. Computer Science › Advanced Neural Network Applications

   Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

   He, Kang, Dong, Fu and Yang · arXiv (Cornell University) · 2018

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   1. Unchecked“Empirically, SFP from scratch outperforms the previous filter pruning methods.”
   2. Unchecked“Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art.”

3. Computer Science › Advanced Neural Network Applications

   Only Train Once: A One-Shot Neural Network Training And Pruning Framework

   Chen, Bo, Ding et al. · arXiv (Cornell University) · 2021

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   1. Unchecked“OTO contains two keys: (i) we partition the parameters of DNNs into zero-invariant groups, enabling us to prune zero groups without affecting the output; and (ii) to promote zero groups, we then formulate a structured-sparsity optimization problem and propos…
   2. Unchecked“To demonstrate the effectiveness of OTO, we train and compress full models simultaneously from scratch without fine-tuning for inference speedup and parameter reduction, and achieve state-of-the-art results on VGG16 for CIFAR10, ResNet50 for CIFAR10 and Bert…

4. Computer Science › Advanced Neural Network Applications

   Efficient Layer Compression Without Pruning

   Wu, Zhu, Fang, Deng and Zhong · IEEE Transactions on Image Processing · 2023

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   1. Unchecked“Experimental results conducted on two datasets demonstrate that our method retains superior performance with a FLOPs reduction of 74.1% for VGG-16 and 54.6% for ResNet-56, respectively.”
   2. Unchecked“In addition, our ELC improves the inference speed by 2× on Jetson AGX Xavier edge device.”

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

   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

   HRank: Filter Pruning using High-Rank Feature Map

   Lin, Ji, Wang et al. · arXiv (Cornell University) · 2020

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   1. Unchecked“Our HRank is inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive.”
   2. Unchecked“For example, with ResNet-110, we achieve a 58.2%-FLOPs reduction by removing 59.2% of the parameters, with only a small loss of 0.14% in top-1 accuracy on CIFAR-10.”
   3. Unchecked“With Res-50, we achieve a 43.8%-FLOPs reduction by removing 36.7% of the parameters, with only a loss of 1.17% in the top-1 accuracy on ImageNet.”

8. Computer Science › Advanced Neural Network Applications

   Reduced storage direct tensor ring decomposition for convolutional neural networks compression

   Gabor and Zdunek · arXiv (Cornell University) · 2024

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   1. Unchecked“The experiments, performed on the CIFAR-10 and ImageNet datasets, clearly demonstrate the efficiency of RSDTR in comparison to other state-of-the-art CNNs compression approaches.”