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

Showing 451500 of 534 papers

TitleStatusHype
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
A pruning method based on the dissimilarity of angle among channels and filtersCode0
Pruning in the Face of AdversariesCode0
ESPN: Extremely Sparse Pruned NetworksCode0
Max-Affine Spline Insights Into Deep Network PruningCode0
Compact Bayesian Neural Networks via pruned MCMC samplingCode0
Cogradient Descent for Dependable LearningCode0
EigenDamage: Structured Pruning in the Kronecker-Factored EigenbasisCode0
A Closer Look at Structured Pruning for Neural Network CompressionCode0
Model Preserving Compression for Neural NetworksCode0
Clustering Convolutional Kernels to Compress Deep Neural NetworksCode0
Efficient Sparse-Winograd Convolutional Neural NetworksCode0
Structured Sparsification with Joint Optimization of Group Convolution and Channel ShuffleCode0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressionsCode0
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
An empirical study of a pruning mechanismCode0
Efficient Incorporation of Multiple Latency Targets in the Once-For-All NetworkCode0
“Learning-Compression” Algorithms for Neural Net PruningCode0
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation InformationCode0
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian SamplingCode0
Causal Explanation of Convolutional Neural NetworksCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
A Framework for Neural Network Pruning Using Gibbs DistributionsCode0
EDAC: Efficient Deployment of Audio Classification Models For COVID-19 DetectionCode0
What to Do When Your Discrete Optimization Is the Size of a Neural Network?Code0
Network Compression via Central FilterCode0
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
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
Class-dependent Compression of Deep Neural NetworksCode0
Can pruning improve certified robustness of neural networks?Code0
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversionCode0
Network Pruning via Feature Shift MinimizationCode0
Network Pruning via Performance MaximizationCode0
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two BenchmarksCode0
Network Pruning via Transformable Architecture SearchCode0
Winning the Lottery with Continuous SparsificationCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
Structured Pruning for Efficient ConvNets via Incremental RegularizationCode0
Neural network is heterogeneous: Phase matters moreCode0
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-TrainingCode0
Neural Network Panning: Screening the Optimal Sparse Network Before TrainingCode0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
Rethinking Network Pruning -- under the Pre-train and Fine-tune ParadigmCode0
Neural Network Pruning by Gradient DescentCode0
Rethinking the Value of Network PruningCode0
Building Efficient ConvNets using Redundant Feature PruningCode0
Neural Network Pruning with Residual-Connections and Limited-DataCode0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource UtilizationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50-2.3 GFLOPsAccuracy78.79Unverified
2ResNet50-1.5 GFLOPsAccuracy78.07Unverified
3ResNet50 2.5 GFLOPSAccuracy78Unverified
4RegX-1.6GAccuracy77.97Unverified
5ResNet50 2.0 GFLOPSAccuracy77.7Unverified
6ResNet50-3G FLOPsAccuracy77.1Unverified
7ResNet50-2G FLOPsAccuracy76.4Unverified
8ResNet50-1G FLOPsAccuracy76.38Unverified
9TAS-pruned ResNet-50Accuracy76.2Unverified
10ResNet50Accuracy75.59Unverified
#ModelMetricClaimedVerifiedStatus
1FeatherTop-1 Accuracy76.93Unverified
2SpartanTop-1 Accuracy76.17Unverified
3ST-3Top-1 Accuracy76.03Unverified
4AC/DCTop-1 Accuracy75.64Unverified
5CSTop-1 Accuracy75.5Unverified
6ProbMaskTop-1 Accuracy74.68Unverified
7STRTop-1 Accuracy74.31Unverified
8DNWTop-1 Accuracy74Unverified
9GMPTop-1 Accuracy73.91Unverified
#ModelMetricClaimedVerifiedStatus
1+U-DML*Inference Time (ms)675.56Unverified
2DenseAccuracy79Unverified
3AC/DCAccuracy78.2Unverified
4Beta-RankAccuracy74.01Unverified
5TAS-pruned ResNet-110Accuracy73.16Unverified
#ModelMetricClaimedVerifiedStatus
1TAS-pruned ResNet-110Accuracy94.33Unverified
2ShuffleNet – QuantisedInference Time (ms)23.15Unverified
3AlexNet – QuantisedInference Time (ms)5.23Unverified
4MobileNet – QuantisedInference Time (ms)4.74Unverified
#ModelMetricClaimedVerifiedStatus
1FFN-ShapleyPrunedAvg #Steps12.05Unverified