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

Showing 501534 of 534 papers

TitleStatusHype
Rethinking the Value of Network PruningCode0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Pruning neural network models for gene regulatory dynamics using data and domain knowledgeCode0
Distilled Pruning: Using Synthetic Data to Win the LotteryCode0
Causal Explanation of Convolutional Neural NetworksCode0
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 RegularizationCode0
Winning the Lottery with Continuous SparsificationCode0
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?Code0
On-Device Neural Language Model Based Word PredictionCode0
Can pruning improve certified robustness of neural networks?Code0
Robust error bounds for quantised and pruned neural networksCode0
Zeroth-Order Adaptive Neuron Alignment Based Pruning without Re-TrainingCode0
Device-Wise Federated Network PruningCode0
SWAP: Sparse Entropic Wasserstein Regression for Robust Network PruningCode0
Same, Same But Different - Recovering Neural Network Quantization Error Through Weight FactorizationCode0
A Framework for Neural Network Pruning Using Gibbs DistributionsCode0
Building Efficient ConvNets using Redundant Feature PruningCode0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled DataCode0
Structured Pruning of Deep Convolutional Neural NetworksCode0
Boosting Large Language Models with Mask Fine-TuningCode0
Adversarial Robustness vs Model Compression, or Both?Code0
What Do Compressed Deep Neural Networks Forget?Code0
OrthoReg: Robust Network Pruning Using Orthonormality RegularizationCode0
Dep-L_0: Improving L_0-based Network Sparsification via Dependency ModelingCode0
DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy CompressionCode0
Supervised Robustness-preserving Data-free Neural Network PruningCode0
Statistical Mechanical Analysis of Neural Network PruningCode0
Studying the impact of magnitude pruning on contrastive learning methodsCode0
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman CodingCode0
B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM PruningCode0
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural NetworksCode0
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image AnalysisCode0
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language UnderstandingCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50-2.3 GFLOPsAccuracy78.79Unverified
2ResNet50-1.5 GFLOPsAccuracy78.07Unverified
3ResNet50 2.5 GFLOPSAccuracy78Unverified
4RegX-1.6GAccuracy77.97Unverified
5ResNet50 2.0 GFLOPSAccuracy77.7Unverified
6ResNet50-3G FLOPsAccuracy77.1Unverified
7ResNet50-2G FLOPsAccuracy76.4Unverified
8ResNet50-1G FLOPsAccuracy76.38Unverified
9TAS-pruned ResNet-50Accuracy76.2Unverified
10ResNet50Accuracy75.59Unverified
#ModelMetricClaimedVerifiedStatus
1FeatherTop-1 Accuracy76.93Unverified
2SpartanTop-1 Accuracy76.17Unverified
3ST-3Top-1 Accuracy76.03Unverified
4AC/DCTop-1 Accuracy75.64Unverified
5CSTop-1 Accuracy75.5Unverified
6ProbMaskTop-1 Accuracy74.68Unverified
7STRTop-1 Accuracy74.31Unverified
8DNWTop-1 Accuracy74Unverified
9GMPTop-1 Accuracy73.91Unverified
#ModelMetricClaimedVerifiedStatus
1+U-DML*Inference Time (ms)675.56Unverified
2DenseAccuracy79Unverified
3AC/DCAccuracy78.2Unverified
4Beta-RankAccuracy74.01Unverified
5TAS-pruned ResNet-110Accuracy73.16Unverified
#ModelMetricClaimedVerifiedStatus
1TAS-pruned ResNet-110Accuracy94.33Unverified
2ShuffleNet – QuantisedInference Time (ms)23.15Unverified
3AlexNet – QuantisedInference Time (ms)5.23Unverified
4MobileNet – QuantisedInference Time (ms)4.74Unverified
#ModelMetricClaimedVerifiedStatus
1FFN-ShapleyPrunedAvg #Steps12.05Unverified