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1,460 claims from 908 papers are on the record. 46 have been checked so far; the other 1,414 have no check with a result yet.

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Keyword: inference acceleration ×Clear all

10 claims from 5 papers

1. Computer Science › Advanced Neural Network Applications

   Pruning Filters for Efficient ConvNets

   Li, Kadav, Đurđanović, Samet and Graf · arXiv (Cornell University) · 2016

   The paper prunes whole filters from convolutional networks that have little effect on accuracy, cutting inference costs for VGG-16 and ResNet-110 on CIFAR10 while retraining to regain close to the original accuracy.

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   1. UncheckedRemoving whole filters from a convolutional network, unlike pruning individual weights, does not leave irregular, sparse connections in the network.“In contrast to pruning weights, this approach does not result in sparse connectivity patterns.”
   2. UncheckedSimple filter pruning cut inference costs by up to 34% for VGG-16 and 38% for ResNet-110 on CIFAR10, with retraining restoring near-original accuracy.“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.”

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

   The paper proposes discrimination-aware channel and kernel pruning to make deep networks smaller and faster, and reports tests on image classification and face recognition.

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   1. UncheckedOn ILSVRC-12, a ResNet-50 pruned to cut 30% of channels scored 0.36% higher Top-1 accuracy than its unpruned baseline.“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. UncheckedAfter pruning with the authors' method, MobileNetV1 and MobileNetV2 run 1.93x and 1.42x faster on a mobile device with negligible loss in performance.“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

   ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

   Luo, Wu and Lin · arXiv (Cornell University) · 2017

   The paper proposes ThiNet, a filter-pruning method that shrinks and speeds up convolutional neural networks, and reports results on VGG-16 and ResNet-50 using the ILSVRC-12 benchmark.

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   1. UncheckedOn ResNet-50, ThiNet is reported to cut over half the parameters and FLOPs at a cost of roughly 1% in top-5 accuracy.“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. UncheckedThe paper frames filter pruning as an optimisation problem and says filters should be chosen using the next layer's statistics, not the current layer's.“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.”

4. 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

   The paper proposes Soft Filter Pruning, which lets pruned filters keep being updated during training, to speed up CNN inference and can be trained from scratch.

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   1. UncheckedThe authors report that their soft filter pruning method, trained from scratch, outperforms earlier filter pruning methods.“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.”

5. 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.”