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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 534 papers

TitleStatusHype
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
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning—0
Finding Deep Local Optima Using Network Pruning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective—0
Class-dependent Compression of Deep Neural NetworksCode0
Defeating Misclassification Attacks Against Transfer Learning—0
Architecture-aware Network Pruning for Vision Quality Applications—0
A Targeted Acceleration and Compression Framework for Low bit Neural Networks—0
Data-Independent Neural Pruning via Coresets—0
Importance Estimation for Neural Network PruningCode0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Towards Compact and Robust Deep Neural Networks—0
A Signal Propagation Perspective for Pruning Neural Networks at InitializationCode0
The Generalization-Stability Tradeoff In Neural Network Pruning—0
Discovering Neural WiringsCode1
Variational Convolutional Neural Network Pruning—0
Learning Sparse Networks Using Targeted DropoutCode0
Pruning-Aware Merging for Efficient Multitask Inference—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