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 126–150 of 534 papers

Title	Date	Tasks	Status	Score
Model compression as constrained optimization, with application to neural nets. Part V: combining compressions	Jul 9, 2021	Additive modelsLow-rank compression	CodeCode Available	5
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI	Apr 21, 2020	Cardiac SegmentationImage Segmentation	CodeCode Available	5
CFSP: An Efficient Structured Pruning Framework for LLMs with Coarse-to-Fine Activation Information	Sep 20, 2024	Network Pruning	CodeCode Available	5
Efficient Structured Pruning and Architecture Searching for Group Convolution	Nov 23, 2018	Domain AdaptationNetwork Pruning	CodeCode Available	5
A Quantization-Friendly Separable Convolution for MobileNets	Mar 22, 2018	Edge-computingGPU	CodeCode Available	5
Causal Explanation of Convolutional Neural Networks	Sep 13, 2021	counterfactualCounterfactual Explanation	CodeCode Available	5
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT	Mar 5, 2023	Federated LearningNetwork Pruning	CodeCode Available	5
Adversarial Fine-tuning of Compressed Neural Networks for Joint Improvement of Robustness and Efficiency	Mar 14, 2024	Adversarial RobustnessModel Compression	CodeCode Available	5
Few Sample Knowledge Distillation for Efficient Network Compression	Dec 5, 2018	Knowledge DistillationNetwork Pruning	CodeCode Available	5
Learning Sparse Networks Using Targeted Dropout	May 31, 2019	Network PruningNeural Network Compression	CodeCode Available	5
Is Complexity Required for Neural Network Pruning? A Case Study on Global Magnitude Pruning	Sep 29, 2022	Network Pruning	CodeCode Available	5
A pruning method based on the dissimilarity of angle among channels and filters	Oct 29, 2022	Network Pruning	CodeCode Available	5
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification	Jun 21, 2021	Lifelong learningNetwork Pruning	CodeCode Available	5
Can pruning improve certified robustness of neural networks?	Jun 15, 2022	Network Pruning	CodeCode Available	5
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps	Sep 7, 2022	Inductive BiasNetwork Pruning	CodeCode Available	5
Building Efficient ConvNets using Redundant Feature Pruning	Feb 21, 2018	Network Pruning	CodeCode Available	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network Pruning	CodeCode Available	5
Investigating the Effect of Network Pruning on Performance and Interpretability	Sep 29, 2024	Network Pruning	CodeCode Available	5
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language Models	Feb 5, 2024	Density EstimationNetwork Pruning	CodeCode Available	5
Network Pruning via Feature Shift Minimization	Jul 6, 2022	Network Pruning	CodeCode Available	5
Boosting Large Language Models with Mask Fine-Tuning	Mar 27, 2025	Language ModelingLanguage Modelling	CodeCode Available	5
HALO: Learning to Prune Neural Networks with Shrinkage	Aug 24, 2020	Network Pruning	CodeCode Available	5
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models	May 22, 2025	Network Pruning	CodeCode Available	5
Adaptive Search-and-Training for Robust and Efficient Network Pruning	Feb 1, 2023	Knowledge DistillationNetwork Pruning	CodeCode Available	5
B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning	Jan 28, 2025	Bayesian OptimizationFace Detection	CodeCode Available	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