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
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks—0
CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution—0
Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs—0
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks—0
Blending Pruning Criteria for Convolutional Neural Networks—0
Mini-batch Coresets for Memory-efficient Training of Large Language Models—0
Meta-Learning with Network Pruning—0
Meta-Learning with Network Pruning for Overfitting Reduction—0
Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning—0
Model Compression Methods for YOLOv5: A Review—0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems—0
Multistage Pruning of CNN Based ECG Classifiers for Edge Devices—0
Multi-Task Network Pruning and Embedded Optimization for Real-time Deployment in ADAS—0
Mutual Information Preserving Neural Network Pruning—0
N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections—0
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm—0
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study—0
Network Automatic Pruning: Start NAP and Take a Nap—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
Network Pruning by Greedy Subnetwork Selection—0
Network Pruning for Low-Rank Binary Index—0
Network Pruning for Low-Rank Binary Indexing—0
Network Pruning Optimization by Simulated Annealing Algorithm—0
Network Pruning Spaces—0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
The Generalization-Stability Tradeoff In Neural Network Pruning—0
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Pruning Before Training May Improve Generalization, Provably—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
When Are Neural Pruning Approximation Bounds Useful?—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
When to Prune? A Policy towards Early Structural Pruning—0
Neural Network Pruning as Spectrum Preserving Process—0
Neural Network Pruning by Cooperative Coevolution—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
Neural Network Pruning for Real-time Polyp Segmentation—0
Neural Network Pruning Through Constrained Reinforcement Learning—0
GD doesn't make the cut: Three ways that non-differentiability affects neural network training—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
New Pruning Method Based on DenseNet Network for Image Classification—0
Automatic Sparse Connectivity Learning for Neural Networks—0
NISP: Pruning Networks using Neuron Importance Score Propagation—0
Automatic Pruning via Structured Lasso with Class-wise Information—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