SOTAVerified

Network Pruning

Network Pruning is a popular approach to reduce a heavy network to obtain a light-weight form by removing redundancy in the heavy network. In this approach, a complex over-parameterized network is first trained, then pruned based on come criterions, and finally fine-tuned to achieve comparable performance with reduced parameters.

Source: Ensemble Knowledge Distillation for Learning Improved and Efficient Networks

Papers

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Showing 451–475 of 534 papers

TitleStatusHype
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian SamplingCode0
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary ParametersCode0
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation—0
Improving Feature Attribution through Input-specific Network Pruning—0
Neural Network Pruning with Residual-Connections and Limited-DataCode0
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks—0
What Do Compressed Deep Neural Networks Forget?Code0
Iteratively Training Look-Up Tables for Network Quantization—0
Cross-Channel Intragroup Sparsity Neural Network—0
Self-Adaptive Network Pruning—0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Pruning from ScratchCode0
Network Pruning for Low-Rank Binary Index—0
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective—0
Finding Deep Local Optima Using Network Pruning—0
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
Class-dependent Compression of Deep Neural NetworksCode0
Defeating Misclassification Attacks Against Transfer Learning—0
Architecture-aware Network Pruning for Vision Quality Applications—0
Data-Independent Neural Pruning via Coresets—0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
Importance Estimation for Neural Network PruningCode0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
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All datasetsImageNetImageNet - ResNet 50 - 90% sparsityCIFAR-100CIFAR-10MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50-2.3 GFLOPsAccuracy78.79—Unverified
2ResNet50-1.5 GFLOPsAccuracy78.07—Unverified
3ResNet50 2.5 GFLOPSAccuracy78—Unverified
4RegX-1.6GAccuracy77.97—Unverified
5ResNet50 2.0 GFLOPSAccuracy77.7—Unverified
6ResNet50-3G FLOPsAccuracy77.1—Unverified
7ResNet50-2G FLOPsAccuracy76.4—Unverified
8ResNet50-1G FLOPsAccuracy76.38—Unverified
9TAS-pruned ResNet-50Accuracy76.2—Unverified
10ResNet50Accuracy75.59—Unverified
#ModelMetricClaimedVerifiedStatus
1FeatherTop-1 Accuracy76.93—Unverified
2SpartanTop-1 Accuracy76.17—Unverified
3ST-3Top-1 Accuracy76.03—Unverified
4AC/DCTop-1 Accuracy75.64—Unverified
5CSTop-1 Accuracy75.5—Unverified
6ProbMaskTop-1 Accuracy74.68—Unverified
7STRTop-1 Accuracy74.31—Unverified
8DNWTop-1 Accuracy74—Unverified
9GMPTop-1 Accuracy73.91—Unverified
#ModelMetricClaimedVerifiedStatus
1+U-DML*Inference Time (ms)675.56—Unverified
2DenseAccuracy79—Unverified
3AC/DCAccuracy78.2—Unverified
4Beta-RankAccuracy74.01—Unverified
5TAS-pruned ResNet-110Accuracy73.16—Unverified
#ModelMetricClaimedVerifiedStatus
1TAS-pruned ResNet-110Accuracy94.33—Unverified
2ShuffleNet – QuantisedInference Time (ms)23.15—Unverified
3AlexNet – QuantisedInference Time (ms)5.23—Unverified
4MobileNet – QuantisedInference Time (ms)4.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FFN-ShapleyPrunedAvg #Steps12.05—Unverified