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 371–380 of 534 papers

Title	Date	Tasks	Status
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning	Jul 14, 2020	Network Pruningreinforcement-learning	—Unverified
Towards Higher Ranks via Adversarial Weight Pruning	Nov 29, 2023	Model CompressionNetwork Pruning	—Unverified
A Unified Approach to Coreset Learning	Nov 4, 2021	Network Pruning	—Unverified
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML	May 23, 2023	Bayesian OptimizationHyperparameter Optimization	—Unverified
Single-path Bit Sharing for Automatic Loss-aware Model Compression	Jan 13, 2021	Model CompressionNetwork Pruning	—Unverified
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning	Mar 2, 2023	Deep Reinforcement LearningMulti-agent Reinforcement Learning	—Unverified
A rescaling-invariant Lipschitz bound based on path-metrics for modern ReLU network parameterizations	May 23, 2024	Generalization BoundsNetwork Pruning	—Unverified
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances	Dec 6, 2022	image-classificationImage Classification	—Unverified
Performance Aware Convolutional Neural Network Channel Pruning for Embedded GPUs	Feb 20, 2020	Model CompressionNetwork Pruning	—Unverified
Personalized Federated Learning for Generative AI-Assisted Semantic Communications	Oct 3, 2024	Federated LearningNetwork Pruning	—Unverified

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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