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 476–500 of 534 papers

TitleStatusHype
An empirical study of a pruning mechanismCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
Stabilizing Elastic Weight Consolidation method in practical ML tasks and using weight importances for neural network pruningCode0
ST-MFNet Mini: Knowledge Distillation-Driven Frame InterpolationCode0
Efficient Incorporation of Multiple Latency Targets in the Once-For-All NetworkCode0
Network Pruning via Feature Shift MinimizationCode0
Network Pruning via Performance MaximizationCode0
EDAC: Efficient Deployment of Audio Classification Models For COVID-19 DetectionCode0
Network Pruning via Transformable Architecture SearchCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation InformationCode0
Neural network is heterogeneous: Phase matters moreCode0
What to Do When Your Discrete Optimization Is the Size of a Neural Network?Code0
Neural Network Panning: Screening the Optimal Sparse Network Before TrainingCode0
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
Structured Pruning for Efficient ConvNets via Incremental RegularizationCode0
Neural Network Pruning by Gradient DescentCode0
Class-dependent Compression of Deep Neural NetworksCode0
Rethinking Network Pruning -- under the Pre-train and Fine-tune ParadigmCode0
Neural Network Pruning with Residual-Connections and Limited-DataCode0
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversionCode0
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource UtilizationCode0
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two BenchmarksCode0
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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