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 521–530 of 534 papers

Title	Date	Tasks	Status	Hype
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning	Nov 15, 2017	Continual LearningNetwork Pruning	CodeCode Available	1
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm	Nov 6, 2017	Network Pruning	—Unverified	0
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design	Aug 26, 2017	Network Pruning	—Unverified	0
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization	Jul 28, 2017	Bayesian OptimizationNetwork Pruning	—Unverified	0
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks	May 23, 2017	Autonomous VehiclesNetwork Pruning	—Unverified	0
Towards thinner convolutional neural networks through Gradually Global Pruning	Mar 29, 2017	Network Pruning	—Unverified	0
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning	Jan 16, 2017	Network Pruning	—Unverified	0
Compact Deep Convolutional Neural Networks With Coarse Pruning	Oct 30, 2016	Network Pruning	—Unverified	0
Pruning Filters for Efficient ConvNets	Aug 31, 2016	Image ClassificationNetwork Pruning	CodeCode Available	2
Reducing the Model Order of Deep Neural Networks Using Information Theory	May 16, 2016	General ClassificationNetwork Pruning	—Unverified	0

Show:10 25 50

← PrevPage 53 of 54Next →

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