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 51–60 of 534 papers

Title	Date	Tasks	Status	Hype	Score
1xN Pattern for Pruning Convolutional Neural Networks	May 31, 2021	CPUNetwork Pruning	CodeCode Available	1	5
How Not to Give a FLOP: Combining Regularization and Pruning for Efficient Inference	Mar 30, 2020	Network Pruning	CodeCode Available	1	5
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness	Sep 2, 2024	Adversarial RobustnessNetwork Pruning	CodeCode Available	1	5
Comparing Rewinding and Fine-tuning in Neural Network Pruning	Mar 5, 2020	Network Pruning	CodeCode Available	1	5
Discovering Neural Wirings	Jun 3, 2019	Feature EngineeringNetwork Pruning	CodeCode Available	1	5
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from Scratch	Mar 21, 2024	Network Pruning	CodeCode Available	1	5
Efficient and Flexible Neural Network Training through Layer-wise Feedback Propagation	Aug 23, 2023	Network PruningTransfer Learning	CodeCode Available	1	5
DHP: Differentiable Meta Pruning via HyperNetworks	Mar 30, 2020	AutoMLDenoising	CodeCode Available	1	5
Lottery Jackpots Exist in Pre-trained Models	Apr 18, 2021	Network Pruning	CodeCode Available	1	5
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers	May 14, 2020	Network Pruning	CodeCode Available	1	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