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 201–225 of 534 papers

Title	Date	Tasks	Status	Score
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoT	Mar 5, 2023	Federated LearningNetwork Pruning	CodeCode Available	5
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flight	Jan 5, 2022	Model Predictive ControlNetwork Pruning	CodeCode Available	5
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains	Nov 13, 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
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding	Oct 1, 2015	CPUGPU	CodeCode Available	5
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network Pruning	Mar 11, 2024	Combinatorial OptimizationNetwork Pruning	CodeCode Available	5
Class-dependent Compression of Deep Neural Networks	Sep 23, 2019	Model CompressionNetwork 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
FastDepth: Fast Monocular Depth Estimation on Embedded Systems	Mar 8, 2019	CPUDecoder	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
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	CodeCode Available	5
Feature Selection for Multivariate Time Series via Network Pruning	Feb 11, 2021	feature selectionNetwork Pruning	CodeCode Available	5
A Quantization-Friendly Separable Convolution for MobileNets	Mar 22, 2018	Edge-computingGPU	CodeCode Available	5
Importance Estimation for Neural Network Pruning	Jun 25, 2019	Network Pruning	CodeCode Available	5
Improving the Transferability of Adversarial Examples via Direction Tuning	Mar 27, 2023	Network Pruning	CodeCode Available	5
Filter Pruning For CNN With Enhanced Linear Representation Redundancy	Oct 10, 2023	channel selectionNetwork Pruning	CodeCode Available	5
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language Understanding	Jan 10, 2023	Natural Language UnderstandingNetwork Pruning	CodeCode Available	5
Interpretations Steered Network Pruning via Amortized Inferred Saliency Maps	Sep 7, 2022	Inductive BiasNetwork 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
Attention-Based Guided Structured Sparsity of Deep Neural Networks	Feb 13, 2018	Network Pruning	CodeCode Available	5
Guiding Evolutionary AutoEncoder Training with Activation-Based Pruning Operators	May 8, 2025	DecoderNetwork Pruning	CodeCode Available	5
HALO: Learning to Prune Neural Networks with Shrinkage	Aug 24, 2020	Network Pruning	CodeCode Available	5
Rethinking Weight Decay For Efficient Neural Network Pruning	Nov 20, 2020	Efficient Neural NetworkNetwork Pruning	CodeCode Available	5
A Framework for Neural Network Pruning Using Gibbs Distributions	Jun 8, 2020	Network Pruning	CodeCode Available	5
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking	Jul 17, 2021	Continual LearningNetwork 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