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 301–350 of 534 papers

TitleStatusHype
Post-training deep neural network pruning via layer-wise calibration—0
Privacy-preserving Learning via Deep Net Pruning—0
Privacy Preserving Stochastic Channel-Based Federated Learning with Neural Network Pruning—0
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network—0
Progressive Local Filter Pruning for Image Retrieval Acceleration—0
Protective Self-Adaptive Pruning to Better Compress DNNs—0
How much pre-training is enough to discover a good subnetwork?—0
Prune Responsibly—0
Pruning a neural network using Bayesian inference—0
Pruning CNN's with linear filter ensembles—0
Pruning Compact ConvNets for Efficient Inference—0
Pruning has a disparate impact on model accuracy—0
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
Putting 3D Spatially Sparse Networks on a Diet—0
Rapid Neural Pruning for Novel Datasets with Set-based Task-Adaptive Meta-Pruning—0
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning—0
RARTS: An Efficient First-Order Relaxed Architecture Search Method—0
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm—0
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks—0
Reducing the Model Order of Deep Neural Networks Using Information Theory—0
Renormalized Sparse Neural Network Pruning—0
Representation and decomposition of functions in DAG-DNNs and structural network pruning—0
Resource-Efficient Neural Networks for Embedded Systems—0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Rethinking Again the Value of Network Pruning -- A Dynamical Isometry Perspective—0
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