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
RGP: Neural Network Pruning through Its Regular Graph Structure—0
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning—0
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks—0
Scalable iterative pruning of large language and vision models using block coordinate descent—0
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks—0
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning—0
Self-Adaptive Network Pruning—0
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization—0
Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations—0
Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers—0
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance—0
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning—0
Softer Pruning, Incremental Regularization—0
SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference—0
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning—0
Streamlining Tensor and Network Pruning in PyTorch—0
Structural Alignment for Network Pruning through Partial Regularization—0
Structurally Prune Anything: Any Architecture, Any Framework, Any Time—0
Structured Deep Neural Network Pruning via Matrix Pivoting—0
Structured Network Pruning by Measuring Filter-wise Interactions—0
Structured Pattern Pruning Using Regularization—0
Structured Pruning Meets Orthogonality—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
The Generalization-Stability Tradeoff In 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