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 401–425 of 534 papers

TitleStatusHype
Achieving Adversarial Robustness via Sparsity—0
A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning—0
Network Pruning via Annealing and Direct Sparsity Control—0
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design—0
Pruning has a disparate impact on model accuracy—0
Troubleshooting Blind Image Quality Models in the Wild—0
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI—0
A relativistic extension of Hopfield neural networks via the mechanical analogy—0
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
Architecture-aware Network Pruning for Vision Quality Applications—0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
Putting 3D Spatially Sparse Networks on a Diet—0
AP: Selective Activation for De-sparsifying Pruned Neural Networks—0
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration—0
A Probabilistic Approach to Neural Network Pruning—0
Rapid Neural Pruning for Novel Datasets with Set-based Task-Adaptive Meta-Pruning—0
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning—0
RARTS: An Efficient First-Order Relaxed Architecture Search Method—0
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm—0
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks—0
Reducing the Model Order of Deep Neural Networks Using Information Theory—0
Renormalized Sparse Neural Network Pruning—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—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