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 351–375 of 534 papers

TitleStatusHype
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning—0
Pruning Before Training May Improve Generalization, Provably—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
GD doesn't make the cut: Three ways that non-differentiability affects neural network training—0
New Pruning Method Based on DenseNet Network for Image Classification—0
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks—0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge—0
Towards Compact and Robust Deep Neural Networks—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Towards Fairness-aware Adversarial Network Pruning—0
Towards Higher Ranks via Adversarial Weight Pruning—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models—0
Towards thinner convolutional neural networks through Gradually Global Pruning—0
Network Pruning via Annealing and Direct Sparsity Control—0
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design—0
Troubleshooting Blind Image Quality Models in the Wild—0
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches—0
Unveiling Invariances via Neural Network Pruning—0
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory—0
Variational Convolutional Neural Network Pruning—0
Verification of Neural Networks: Enhancing Scalability through Pruning—0
Show:102550
← PrevPage 15 of 22Next →
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