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 171–180 of 534 papers

Title	Date	Tasks	Status	Score
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
Learning Sparse Networks Using Targeted Dropout	May 31, 2019	Network PruningNeural Network Compression	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
Structured Sparsification with Joint Optimization of Group Convolution and Channel Shuffle	Feb 19, 2020	Network Pruning	CodeCode Available	5
Importance Estimation for Neural Network Pruning	Jun 25, 2019	Network Pruning	CodeCode Available	5
Improving Generalization in Meta-Learning via Meta-Gradient Augmentation	Jun 14, 2023	Few-Shot LearningMemorization	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
HALO: Learning to Prune Neural Networks with Shrinkage	Aug 24, 2020	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