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 251–300 of 534 papers

TitleStatusHype
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?Code0
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?Code0
A Fair Loss Function for Network PruningCode0
A pruning method based on the dissimilarity of angle among channels and filtersCode0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
PP-StructureV2: A Stronger Document Analysis SystemCode0
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude PruningCode0
Neural Network Panning: Screening the Optimal Sparse Network Before TrainingCode0
Learning ASR pathways: A sparse multilingual ASR model—0
One-shot Network Pruning at Initialization with Discriminative Image Patches—0
CWP: Instance complexity weighted channel-wise soft masks for network pruning—0
Interpretations Steered Network Pruning via Amortized Inferred Saliency MapsCode0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—0
N2NSkip: Learning Highly Sparse Networks using Neuron-to-Neuron Skip Connections—0
DASS: Differentiable Architecture Search for Sparse neural networksCode0
Network Pruning via Feature Shift MinimizationCode0
Studying the impact of magnitude pruning on contrastive learning methodsCode0
Renormalized Sparse Neural Network Pruning—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches—0
Can pruning improve certified robustness of neural networks?Code0
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images—0
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
Pruning for Feature-Preserving Circuits in CNNsCode0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge—0
Pruning has a disparate impact on model accuracy—0
Compression-aware Training of Neural Networks using Frank-WolfeCode0
Hyperparameter Optimization with Neural Network Pruning—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Neural Network Pruning by Cooperative Coevolution—0
Supervised Robustness-preserving Data-free Neural Network PruningCode0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning—0
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning—0
Pruning-aware Sparse Regularization for Network PruningCode0
Why Does DARTS Miss the Target, and How Do We Aim to Fix It?—0
Automatic Sparse Connectivity Learning for Neural Networks—0
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flightCode0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Network Compression via Central FilterCode0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—0
Putting 3D Spatially Sparse Networks on a Diet—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