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 326–350 of 534 papers

TitleStatusHype
Network Pruning for Low-Rank Binary Indexing—0
Network Pruning Optimization by Simulated Annealing Algorithm—0
Network Pruning Spaces—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
Pruning Before Training May Improve Generalization, Provably—0
Automatic Sparse Connectivity Learning for Neural Networks—0
When to Prune? A Policy towards Early Structural Pruning—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Why Does DARTS Miss the Target, and How Do We Aim to Fix It?—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
GD doesn't make the cut: Three ways that non-differentiability affects neural network training—0
Neural Network Pruning as Spectrum Preserving Process—0
Neural Network Pruning by Cooperative Coevolution—0
New Pruning Method Based on DenseNet Network for Image Classification—0
Neural Network Pruning for Real-time Polyp Segmentation—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks—0
Automatic Pruning via Structured Lasso with Class-wise Information—0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
NISP: Pruning Networks using Neuron Importance Score Propagation—0
Automated Model Compression by Jointly Applied Pruning and Quantization—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