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
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters	Dec 1, 2019	Network 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
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
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
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains	Nov 13, 2020	Network Pruning	CodeCode Available	5
Neural Network Panning: Screening the Optimal Sparse Network Before Training	Sep 27, 2022	Network PruningScheduling	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
Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification	Jun 21, 2021	Lifelong learningNetwork Pruning	CodeCode Available	5
Investigating the Effect of Network Pruning on Performance and Interpretability	Sep 29, 2024	Network Pruning	CodeCode Available	5
Device-Wise Federated Network Pruning	Jan 1, 2024	Edge-computingFederated Learning	CodeCode Available	5
Dep-L_0: Improving L_0-based Network Sparsification via Dependency Modeling	Jun 30, 2021	Network PruningVariational Inference	CodeCode Available	5
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps	Sep 7, 2022	Inductive BiasNetwork 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
Importance Estimation for Neural Network Pruning	Jun 25, 2019	Network Pruning	CodeCode Available	5
âLearning-Compressionâ Algorithms for Neural Net Pruning	Jun 1, 2018	Model CompressionNetwork Pruning	CodeCode Available	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	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
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language Models	May 22, 2025	Network Pruning	CodeCode Available	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network 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