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 26–50 of 534 papers

TitleStatusHype
Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery—0
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-TrainingCode0
Mutual Information Preserving Neural Network Pruning—0
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance—0
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language ModelsCode0
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning—0
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM PruningCode1
Personalized Federated Learning for Generative AI-Assisted Semantic Communications—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Investigating the Effect of Network Pruning on Performance and InterpretabilityCode0
MaskLLM: Learnable Semi-Structured Sparsity for Large Language ModelsCode2
OATS: Outlier-Aware Pruning Through Sparse and Low Rank DecompositionCode1
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation InformationCode0
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial RobustnessCode1
3D Point Cloud Network Pruning: When Some Weights Do not MatterCode0
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs—0
Confident magnitude-based neural network pruning—0
Investigating Sparsity in Recurrent Neural NetworksCode1
Mini-batch Coresets for Memory-efficient Training of Large Language Models—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—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