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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 426–450 of 534 papers

TitleStatusHype
Representation and decomposition of functions in DAG-DNNs and structural network pruning—0
Resource-Efficient Neural Networks for Embedded Systems—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective—0
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches—0
Unveiling Invariances via Neural Network Pruning—0
A "Network Pruning Network" Approach to Deep Model Compression—0
RGP: Neural Network Pruning through Its Regular Graph Structure—0
An End-to-End Network Pruning Pipeline with Sparsity Enforcement—0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—0
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning—0
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory—0
Variational Convolutional Neural Network Pruning—0
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Scalable iterative pruning of large language and vision models using block coordinate descent—0
Verification of Neural Networks: Enhancing Scalability through Pruning—0
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks—0
Joint Device-Edge Inference over Wireless Links with Pruning—0
Deep Network Pruning: A Comparative Study on CNNs in Face Recognition—0
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images—0
An Efficient Row-Based Sparse Fine-Tuning—0
Defeating Misclassification Attacks Against Transfer Learning—0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
DBP: Discrimination Based Block-Level Pruning for Deep Model Acceleration—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