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 301–325 of 534 papers

TitleStatusHype
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory—0
Self-supervised Feature-Gate Coupling for Dynamic Network PruningCode0
A Unified Approach to Coreset Learning—0
Neural network is heterogeneous: Phase matters moreCode0
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach—0
How I Learned to Stop Worrying and Love RetrainingCode0
RGP: Neural Network Pruning through Its Regular Graph Structure—0
When to Prune? A Policy towards Early Structural Pruning—0
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
Differentiable Network Pruning for Microcontrollers—0
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices—0
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint SearchCode0
Efficient Ensembles of Graph Neural Networks—0
Structured Pruning Meets Orthogonality—0
Can network pruning benefit deep learning under label noise?—0
Automated Channel Pruning with Learned Importance—0
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains—0
Network Pruning Optimization by Simulated Annealing Algorithm—0
Global Magnitude Pruning With Minimum Threshold Is All We NeedCode0
Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
Stabilizing Elastic Weight Consolidation method in practical ML tasks and using weight importances for neural network pruningCode0
Reproducibility Study: Comparing Rewinding and Fine-tuning in Neural Network PruningCode0
Structured Pattern Pruning Using Regularization—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