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 251–275 of 534 papers

TitleStatusHype
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
Dynamic parameter reallocation improves trainability of deep convolutional networks—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Mini-batch Coresets for Memory-efficient Training of Large Language Models—0
Meta-Learning with Network Pruning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Model Compression Methods for YOLOv5: A Review—0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing—0
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems—0
Multistage Pruning of CNN Based ECG Classifiers for Edge Devices—0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Mutual Information Preserving Neural Network Pruning—0
N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections—0
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm—0
Network Automatic Pruning: Start NAP and Take a Nap—0
Network Pruning by Greedy Subnetwork Selection—0
Network Pruning for Low-Rank Binary Index—0
Network Pruning for Low-Rank Binary Indexing—0
Network Pruning Optimization by Simulated Annealing Algorithm—0
Network Pruning Spaces—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Neural Network Pruning as Spectrum Preserving Process—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