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 41–50 of 534 papers

Title	Date	Tasks	Status	Hype	Score
Automatic Neural Network Pruning that Efficiently Preserves the Model Accuracy	Nov 18, 2021	Network Pruning	CodeCode Available	1	5
Automatic Network Pruning via Hilbert-Schmidt Independence Criterion Lasso under Information Bottleneck Principle	Jan 1, 2023	Network Pruning	CodeCode Available	1	5
Recent Advances on Neural Network Pruning at Initialization	Mar 11, 2021	BenchmarkingNetwork Pruning	CodeCode Available	1	5
Discovering Neural Wirings	Jun 3, 2019	Feature EngineeringNetwork Pruning	CodeCode Available	1	5
Layer-adaptive sparsity for the Magnitude-based Pruning	Oct 15, 2020	image-classificationImage Classification	CodeCode Available	1	5
An Information Theory-inspired Strategy for Automatic Network Pruning	Aug 19, 2021	AutoMLModel Compression	CodeCode Available	1	5
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise Sparsity	Aug 29, 2020	GPUNetwork Pruning	CodeCode Available	1	5
APP: Anytime Progressive Pruning	Apr 4, 2022	Network PruningSparse Learning	CodeCode Available	1	5
Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization	Oct 11, 2020	Network Pruningtext-classification	CodeCode Available	1	5
Comparing Rewinding and Fine-tuning in Neural Network Pruning	Mar 5, 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