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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 376–400 of 534 papers

Title	Date	Tasks	Status	Hype
Meta-Learning with Network Pruning	Jul 7, 2020	Few-Shot LearningMeta-Learning	—Unverified	0
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	Jul 6, 2020	Efficient Neural NetworkNetwork Pruning	CodeCode Available	1
Weight-dependent Gates for Network Pruning	Jul 4, 2020	Network Pruning	—Unverified	0
Statistical Mechanical Analysis of Neural Network Pruning	Jun 30, 2020	Computational EfficiencyNetwork Pruning	CodeCode Available	0
ESPN: Extremely Sparse Pruned Networks	Jun 28, 2020	Network Pruning	CodeCode Available	0
Rapid Structural Pruning of Neural Networks with Set-based Task-Adaptive Meta-Pruning	Jun 22, 2020	Cloud ComputingNetwork Pruning	—Unverified	0
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble Distillation	Jun 20, 2020	image-classificationImage Classification	CodeCode Available	1
Cogradient Descent for Bilinear Optimization	Jun 16, 2020	Image ReconstructionNetwork Pruning	—Unverified	0
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration	Jun 12, 2020	BIG-bench Machine LearningNetwork Pruning	—Unverified	0
A Framework for Neural Network Pruning Using Gibbs Distributions	Jun 8, 2020	Network Pruning	CodeCode Available	0
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression	Jun 7, 2020	Domain AdaptationKnowledge Distillation	—Unverified	0
Weight Pruning via Adaptive Sparsity Loss	Jun 4, 2020	image-classificationImage Classification	CodeCode Available	1
Shapley Value as Principled Metric for Structured Network Pruning	Jun 2, 2020	Network Pruning	CodeCode Available	1
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks	May 28, 2020	Network PruningQuantization	—Unverified	0
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study	May 23, 2020	BenchmarkingNetwork Pruning	—Unverified	0
MicroNet for Efficient Language Modeling	May 16, 2020	Knowledge DistillationLanguage Modeling	CodeCode Available	1
A flexible, extensible software framework for model compression based on the LC algorithm	May 15, 2020	BIG-bench Machine LearningLow-rank compression	—Unverified	0
Movement Pruning: Adaptive Sparsity by Fine-Tuning	May 15, 2020	Network PruningTransfer Learning	CodeCode Available	1
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers	May 14, 2020	Network Pruning	CodeCode Available	1
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI	May 13, 2020	Explainable Artificial Intelligence (XAI)Face Recognition	—Unverified	0
Compact Neural Representation Using Attentive Network Pruning	May 10, 2020	Network Pruning	—Unverified	0
GPU Acceleration of Sparse Neural Networks	May 9, 2020	BIG-bench Machine LearningGPU	—Unverified	0
Streamlining Tensor and Network Pruning in PyTorch	Apr 28, 2020	Model CompressionNetwork Pruning	—Unverified	0
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI	Apr 21, 2020	Cardiac SegmentationImage Segmentation	CodeCode Available	0
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio	Apr 6, 2020	image-classificationImage Classification	CodeCode Available	1

Show:10 25 50

← PrevPage 16 of 22Next →

All datasets ImageNet ImageNet - ResNet 50 - 90% sparsity CIFAR-100 CIFAR-10 MNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	ResNet50-2.3 GFLOPs	Accuracy	78.79	—	Unverified
2	ResNet50-1.5 GFLOPs	Accuracy	78.07	—	Unverified
3	ResNet50 2.5 GFLOPS	Accuracy	78	—	Unverified
4	RegX-1.6G	Accuracy	77.97	—	Unverified
5	ResNet50 2.0 GFLOPS	Accuracy	77.7	—	Unverified
6	ResNet50-3G FLOPs	Accuracy	77.1	—	Unverified
7	ResNet50-2G FLOPs	Accuracy	76.4	—	Unverified
8	ResNet50-1G FLOPs	Accuracy	76.38	—	Unverified
9	TAS-pruned ResNet-50	Accuracy	76.2	—	Unverified
10	ResNet50	Accuracy	75.59	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Feather	Top-1 Accuracy	76.93	—	Unverified
2	Spartan	Top-1 Accuracy	76.17	—	Unverified
3	ST-3	Top-1 Accuracy	76.03	—	Unverified
4	AC/DC	Top-1 Accuracy	75.64	—	Unverified
5	CS	Top-1 Accuracy	75.5	—	Unverified
6	ProbMask	Top-1 Accuracy	74.68	—	Unverified
7	STR	Top-1 Accuracy	74.31	—	Unverified
8	DNW	Top-1 Accuracy	74	—	Unverified
9	GMP	Top-1 Accuracy	73.91	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	+U-DML*	Inference Time (ms)	675.56	—	Unverified
2	Dense	Accuracy	79	—	Unverified
3	AC/DC	Accuracy	78.2	—	Unverified
4	Beta-Rank	Accuracy	74.01	—	Unverified
5	TAS-pruned ResNet-110	Accuracy	73.16	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	TAS-pruned ResNet-110	Accuracy	94.33	—	Unverified
2	ShuffleNet – Quantised	Inference Time (ms)	23.15	—	Unverified
3	AlexNet – Quantised	Inference Time (ms)	5.23	—	Unverified
4	MobileNet – Quantised	Inference Time (ms)	4.74	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	FFN-ShapleyPruned	Avg #Steps	12.05	—	Unverified