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 176–200 of 534 papers

Title	Date	Tasks	Status	Score
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI	Apr 21, 2020	Cardiac SegmentationImage Segmentation	CodeCode Available	5
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language Models	Oct 17, 2024	Inference OptimizationNetwork Pruning	CodeCode Available	5
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle	Feb 19, 2020	Network Pruning	CodeCode Available	5
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification	Jun 21, 2021	Lifelong learningNetwork Pruning	CodeCode Available	5
EDAC: Efficient Deployment of Audio Classification Models For COVID-19 Detection	Sep 11, 2023	Audio ClassificationNetwork Pruning	CodeCode Available	5
Adaptive Search-and-Training for Robust and Efficient Network Pruning	Feb 1, 2023	Knowledge DistillationNetwork Pruning	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
Device-Wise Federated Network Pruning	Jan 1, 2024	Edge-computingFederated Learning	CodeCode Available	5
Network Pruning via Feature Shift Minimization	Jul 6, 2022	Network Pruning	CodeCode Available	5
Dep-L_0: Improving L_0-based Network Sparsification via Dependency Modeling	Jun 30, 2021	Network PruningVariational Inference	CodeCode Available	5
Investigating the Effect of Network Pruning on Performance and Interpretability	Sep 29, 2024	Network Pruning	CodeCode Available	5
Few Sample Knowledge Distillation for Efficient Network Compression	Dec 5, 2018	Knowledge DistillationNetwork Pruning	CodeCode Available	5
Importance Estimation for Neural Network Pruning	Jun 25, 2019	Network Pruning	CodeCode Available	5
Efficient Sparse-Winograd Convolutional Neural Networks	Feb 18, 2018	Network Pruning	CodeCode Available	5
Boosting Large Language Models with Mask Fine-Tuning	Mar 27, 2025	Language ModelingLanguage Modelling	CodeCode Available	5
DeepSZ: A Novel Framework to Compress Deep Neural Networks by Using Error-Bounded Lossy Compression	Jan 26, 2019	Network PruningNeural Network Compression	CodeCode Available	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	CodeCode Available	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network Pruning	CodeCode Available	5
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators	May 8, 2025	DecoderNetwork Pruning	CodeCode Available	5
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding	Oct 1, 2015	CPUGPU	CodeCode Available	5
HALO: Learning to Prune Neural Networks with Shrinkage	Aug 24, 2020	Network Pruning	CodeCode Available	5
Class-dependent Compression of Deep Neural Networks	Sep 23, 2019	Model CompressionNetwork Pruning	CodeCode Available	5
On Designing Light-Weight Object Trackers through Network Pruning: Use CNNs or Transformers?	Nov 24, 2022	Network PruningObject	CodeCode Available	5
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models	May 22, 2025	Network Pruning	CodeCode Available	5
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps	Sep 7, 2022	Inductive BiasNetwork Pruning	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