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 251–260 of 534 papers

Title	Date	Tasks	Status	Hype
Neural Network Pruning Through Constrained Reinforcement Learning	Oct 16, 2021	image-classificationImage Classification	—Unverified	0
Differentiable Network Pruning for Microcontrollers	Oct 15, 2021	Model CompressionNetwork Pruning	—Unverified	0
Training Deep Neural Networks with Joint Quantization and Pruning of Weights and Activations	Oct 15, 2021	Network PruningQuantization	CodeCode Available	1
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices	Oct 11, 2021	Knowledge DistillationNetwork Pruning	—Unverified	0
ABCP: Automatic Block-wise and Channel-wise Network Pruning via Joint Search	Oct 8, 2021	Deep Reinforcement LearningNetwork Pruning	CodeCode Available	0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)	Oct 1, 2021	Dimensionality ReductionGraph Representation Learning	CodeCode Available	1
Global Magnitude Pruning With Minimum Threshold Is All We Need	Sep 29, 2021	AllNetwork Pruning	CodeCode Available	0
Can network pruning benefit deep learning under label noise?	Sep 29, 2021	Deep LearningNetwork Pruning	—Unverified	0
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains	Sep 29, 2021	Network Pruning	—Unverified	0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning	Sep 29, 2021	Image Super-ResolutionKnowledge Distillation	—Unverified	0

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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