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 101–150 of 534 papers

TitleStatusHype
Iterative Soft Shrinkage Learning for Efficient Image Super-ResolutionCode1
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMsCode1
1xN Pattern for Pruning Convolutional Neural NetworksCode1
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networksCode1
Efficient and Flexible Neural Network Training through Layer-wise Feedback PropagationCode1
EfficientTDNN: Efficient Architecture Search for Speaker RecognitionCode1
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial RobustnessCode1
Channel Gating Neural NetworksCode1
Network Pruning via Feature Shift MinimizationCode0
Structured Sparsification with Joint Optimization of Group Convolution and Channel ShuffleCode0
Network Pruning via Performance MaximizationCode0
Reproducibility Study: Comparing Rewinding and Fine-tuning in Neural Network PruningCode0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
Max-Affine Spline Insights Into Deep Network PruningCode0
A Fair Loss Function for Network PruningCode0
Compact Bayesian Neural Networks via pruned MCMC samplingCode0
Cogradient Descent for Dependable LearningCode0
A Signal Propagation Perspective for Pruning Neural Networks at InitializationCode0
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?Code0
Clustering Convolutional Kernels to Compress Deep Neural NetworksCode0
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language ModelsCode0
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint SearchCode0
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural NetworksCode0
Network Pruning via Transformable Architecture SearchCode0
Network Compression via Central FilterCode0
LEAN: graph-based pruning for convolutional neural networks by extracting longest chainsCode0
Efficient Structured Pruning and Architecture Searching for Group ConvolutionCode0
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation InformationCode0
Learning Sparse Networks Using Targeted DropoutCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
Causal Explanation of Convolutional Neural NetworksCode0
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and EfficiencyCode0
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTCode0
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRICode0
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language ModelsCode0
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment ClassificationCode0
A pruning method based on the dissimilarity of angle among channels and filtersCode0
Can pruning improve certified robustness of neural networks?Code0
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude PruningCode0
Investigating the Effect of Network Pruning on Performance and InterpretabilityCode0
Building Efficient ConvNets using Redundant Feature PruningCode0
Interpretations Steered Network Pruning via Amortized Inferred Saliency MapsCode0
Few Sample Knowledge Distillation for Efficient Network CompressionCode0
Neural Architecture Codesign for Fast Physics ApplicationsCode0
Boosting Large Language Models with Mask Fine-TuningCode0
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language ModelsCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM PruningCode0
3D Point Cloud Network Pruning: When Some Weights Do not MatterCode0
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image AnalysisCode0
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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