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 401–425 of 534 papers

TitleStatusHype
OrthoReg: Robust Network Pruning Using Orthonormality RegularizationCode0
SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference—0
HALO: Learning to Prune Neural Networks with ShrinkageCode0
RARTS: An Efficient First-Order Relaxed Architecture Search Method—0
Hierarchical Action Classification with Network Pruning—0
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning—0
Meta-Learning with Network Pruning—0
Weight-dependent Gates for Network Pruning—0
Statistical Mechanical Analysis of Neural Network PruningCode0
ESPN: Extremely Sparse Pruned NetworksCode0
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning—0
Cogradient Descent for Bilinear Optimization—0
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration—0
A Framework for Neural Network Pruning Using Gibbs DistributionsCode0
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression—0
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks—0
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI—0
Compact Neural Representation Using Attentive Network Pruning—0
GPU Acceleration of Sparse Neural Networks—0
Streamlining Tensor and Network Pruning in PyTorch—0
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRICode0
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle—0
Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training—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