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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: ResNet ×Clear all

11 claims from 7 papers

1. Computer Science › Advanced Neural Network Applications

   Deep Residual Learning for Image Recognition

   He, Zhang, Ren and Sun · arXiv (Cornell University) · 2015

   The paper introduces residual learning, which eases the training of much deeper neural networks, and reports leading results on ImageNet and COCO image recognition tasks in 2015.

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   1. UncheckedAn ensemble of residual networks reached 3.57% error on the ImageNet test set, according to the paper.“An ensemble of these residual nets achieves 3.57% error on the ImageNet test set.”
   2. UncheckedThe authors say that using their very deep residual networks alone gave a 28% relative improvement on the COCO object detection dataset.“Solely due to our extremely deep representations, we obtain a 28% relative improvement on the COCO object detection dataset.”

2. 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.”

3. Computer Science › Advanced Neural Network Applications

   Picking Winning Tickets Before Training by Preserving Gradient Flow

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

   Unchecked2 claims
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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

   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

   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.”

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

   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.”