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 326–350 of 534 papers

TitleStatusHype
Causal Explanation of Convolutional Neural NetworksCode0
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization—0
Multistage Pruning of CNN Based ECG Classifiers for Edge Devices—0
New Pruning Method Based on DenseNet Network for Image Classification—0
Pruning in the Face of AdversariesCode0
Pruning vs XNOR-Net: A Comprehensive Study of Deep Learning for Audio Classification on Edge-devicesCode0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
How much pre-training is enough to discover a good subnetwork?—0
Model Preserving Compression for Neural NetworksCode0
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and MaskingCode0
Blending Pruning Criteria for Convolutional Neural Networks—0
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions—0
Weight Reparametrization for Budget-Aware Network Pruning—0
Connectivity Matters: Neural Network Pruning Through the Lens of Effective SparsityCode0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Dep-L_0: Improving L_0-based Network Sparsification via Dependency ModelingCode0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment ClassificationCode0
Cogradient Descent for Dependable LearningCode0
Network Pruning via Performance MaximizationCode0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
A Probabilistic Approach to Neural Network Pruning—0
Troubleshooting Blind Image Quality Models in the Wild—0
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning—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