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 125 of 534 papers

TitleStatusHype
Exploring GLU Expansion Ratios: A Study of Structured Pruning in LLaMA-3.2 ModelsCode5
Structured Pruning for Deep Convolutional Neural Networks: A surveyCode4
DepGraph: Towards Any Structural PruningCode4
MaskLLM: Learnable Semi-Structured Sparsity for Large Language ModelsCode2
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and RecommendationsCode2
A Simple and Effective Pruning Approach for Large Language ModelsCode2
Pruning Filters for Efficient ConvNetsCode2
ReplaceMe: Network Simplification via Layer Pruning and Linear TransformationsCode1
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM PruningCode1
OATS: Outlier-Aware Pruning Through Sparse and Low Rank DecompositionCode1
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial RobustnessCode1
Investigating Sparsity in Recurrent Neural NetworksCode1
Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight PruningCode1
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from ScratchCode1
Fluctuation-based Adaptive Structured Pruning for Large Language ModelsCode1
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median CriterionCode1
Beyond Size: How Gradients Shape Pruning Decisions in Large Language ModelsCode1
Dynamic Sparse No Training: Training-Free Fine-tuning for Sparse LLMsCode1
Outlier Weighed Layerwise Sparsity (OWL): A Missing Secret Sauce for Pruning LLMs to High SparsityCode1
Feather: An Elegant Solution to Effective DNN SparsificationCode1
Efficient and Flexible Neural Network Training through Layer-wise Feedback PropagationCode1
Pruning vs Quantization: Which is Better?Code1
How Sparse Can We Prune A Deep Network: A Fundamental Limit ViewpointCode1
Lottery Tickets in Evolutionary Optimization: On Sparse Backpropagation-Free TrainabilityCode1
Show:102550
← PrevPage 1 of 22Next →

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