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 151–175 of 534 papers

Title	Date	Tasks	Status	Score
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image Analysis	Apr 15, 2023	Image ClassificationMedical Image Analysis	CodeCode Available	5
An empirical study of a pruning mechanism	Jan 1, 2021	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
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains	Nov 13, 2020	Network Pruning	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
Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion	Dec 18, 2019	Continual LearningDiversity	CodeCode Available	5
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the Edge	May 20, 2025	Intrusion DetectionNetwork Pruning	CodeCode Available	5
How I Learned to Stop Worrying and Love Retraining	Nov 1, 2021	Network Pruning	CodeCode Available	5
Few Sample Knowledge Distillation for Efficient Network Compression	Dec 5, 2018	Knowledge DistillationNetwork Pruning	CodeCode Available	5
Efficient Structured Pruning and Architecture Searching for Group Convolution	Nov 23, 2018	Domain AdaptationNetwork Pruning	CodeCode Available	5
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers	Dec 23, 2021	Dialect IdentificationGPU	CodeCode Available	5
Distilled Pruning: Using Synthetic Data to Win the Lottery	Jul 7, 2023	Efficient Neural NetworkModel Compression	CodeCode Available	5
Investigating the Effect of Network Pruning on Performance and Interpretability	Sep 29, 2024	Network 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
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	Dec 1, 2019	Network Pruning	CodeCode Available	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network Pruning	CodeCode Available	5
DiffPrune: Neural Network Pruning with Deterministic Approximate Binary Gates and L_0 Regularization	Dec 7, 2020	Image ClassificationModel Selection	CodeCode Available	5
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI Feedback	Jan 20, 2023	Model CompressionNetwork Pruning	CodeCode Available	5
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two Benchmarks	Nov 15, 2023	BenchmarkingNetwork Pruning	CodeCode Available	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	CodeCode Available	5
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps	Sep 7, 2022	Inductive BiasNetwork Pruning	CodeCode Available	5
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification	Jun 21, 2021	Lifelong learningNetwork Pruning	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
Learning Sparse Networks Using Targeted Dropout	May 31, 2019	Network PruningNeural Network Compression	CodeCode Available	5
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural Networks	Dec 27, 2022	Network PruningNeural Network Compression	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