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 391–400 of 534 papers

Title	Date	Tasks	Status	Hype
MicroNet for Efficient Language Modeling	May 16, 2020	Knowledge DistillationLanguage Modeling	CodeCode Available	1
A flexible, extensible software framework for model compression based on the LC algorithm	May 15, 2020	BIG-bench Machine LearningLow-rank compression	—Unverified	0
Movement Pruning: Adaptive Sparsity by Fine-Tuning	May 15, 2020	Network PruningTransfer Learning	CodeCode Available	1
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked Layers	May 14, 2020	Network Pruning	CodeCode Available	1
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI	May 13, 2020	Explainable Artificial Intelligence (XAI)Face Recognition	—Unverified	0
Compact Neural Representation Using Attentive Network Pruning	May 10, 2020	Network Pruning	—Unverified	0
GPU Acceleration of Sparse Neural Networks	May 9, 2020	BIG-bench Machine LearningGPU	—Unverified	0
Streamlining Tensor and Network Pruning in PyTorch	Apr 28, 2020	Model CompressionNetwork Pruning	—Unverified	0
L-CO-Net: Learned Condensation-Optimization Network for Clinical Parameter Estimation from Cardiac Cine MRI	Apr 21, 2020	Cardiac SegmentationImage Segmentation	CodeCode Available	0
Network Adjustment: Channel Search Guided by FLOPs Utilization Ratio	Apr 6, 2020	image-classificationImage Classification	CodeCode Available	1

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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