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 101–125 of 534 papers

Title	Date	Tasks	Status	Hype	Score
Filter Sketch for Network Pruning	Jan 23, 2020	Network Pruning	CodeCode Available	1	5
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion	Nov 28, 2023	Face DetectionNetwork Pruning	CodeCode Available	1	5
Shapley Value as Principled Metric for Structured Network Pruning	Jun 2, 2020	Network Pruning	CodeCode Available	1	5
Fluctuation-based Adaptive Structured Pruning for Large Language Models	Dec 19, 2023	Network Pruning	CodeCode Available	1	5
Spartan: Differentiable Sparsity via Regularized Transportation	May 27, 2022	Network Pruning	CodeCode Available	1	5
Specializing Pre-trained Language Models for Better Relational Reasoning via Network Pruning	Jul 1, 2022	Network PruningRelational Reasoning	CodeCode Available	1	5
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness	Sep 2, 2024	Adversarial RobustnessNetwork Pruning	CodeCode Available	1	5
Channel Gating Neural Networks	May 29, 2018	Knowledge DistillationNetwork Pruning	CodeCode Available	1	5
A flexible, extensible software framework for model compression based on the LC algorithm	May 15, 2020	BIG-bench Machine LearningLow-rank compression	CodeCode Available	0	5
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification	Jun 21, 2021	Lifelong learningNetwork Pruning	CodeCode Available	0	5
Investigating the Effect of Network Pruning on Performance and Interpretability	Sep 29, 2024	Network Pruning	CodeCode Available	0	5
Efficient Model-Based Deep Learning via Network Pruning and Fine-Tuning	Nov 3, 2023	Deep LearningImage Reconstruction	CodeCode Available	0	5
Few Sample Knowledge Distillation for Efficient Network Compression	Dec 5, 2018	Knowledge DistillationNetwork Pruning	CodeCode Available	0	5
A Fair Loss Function for Network Pruning	Nov 18, 2022	FairnessLesion Classification	CodeCode Available	0	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	CodeCode Available	0	5
Compact Bayesian Neural Networks via pruned MCMC sampling	Jan 12, 2025	Network PruningUncertainty Quantification	CodeCode Available	0	5
Cogradient Descent for Dependable Learning	Jun 20, 2021	Image InpaintingImage Reconstruction	CodeCode Available	0	5
A Signal Propagation Perspective for Pruning Neural Networks at Initialization	Jun 14, 2019	image-classificationImage Classification	CodeCode Available	0	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network Pruning	CodeCode Available	0	5
Are Straight-Through gradients and Soft-Thresholding all you need for Sparse Training?	Dec 2, 2022	AllImage Classification	CodeCode Available	0	5
Reproducibility Study: Comparing Rewinding and Fine-tuning in Neural Network Pruning	Sep 20, 2021	Network Pruning	CodeCode Available	0	5
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search	Oct 8, 2021	Deep Reinforcement LearningNetwork Pruning	CodeCode Available	0	5
Clustering Convolutional Kernels to Compress Deep Neural Networks	Sep 1, 2018	ClusteringGeneral Classification	CodeCode Available	0	5
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning	Sep 29, 2022	Network Pruning	CodeCode Available	0	5
Importance Estimation for Neural Network Pruning	Jun 25, 2019	Network Pruning	CodeCode Available	0	5

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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