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 351–360 of 534 papers

Title	Date	Tasks	Status
Towards Compact and Robust Deep Neural Networks	Jun 14, 2019	Adversarial RobustnessNetwork Pruning	—Unverified
Automated Channel Pruning with Learned Importance	Sep 29, 2021	DenoisingGPU	—Unverified
NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches	Feb 18, 2025	Deep Reinforcement LearningGraph Neural Network	—Unverified
Auto Graph Encoder-Decoder for Neural Network Pruning	Nov 25, 2020	DecoderModel Compression	—Unverified
Data-Independent Neural Pruning via Coresets	Jul 9, 2019	Model CompressionNetwork Pruning	—Unverified
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning	Jun 3, 2024	Model CompressionNetwork Pruning	—Unverified
Towards Fairness-aware Adversarial Network Pruning	Jan 1, 2023	FairnessNetwork Pruning	—Unverified
One-shot Network Pruning at Initialization with Discriminative Image Patches	Sep 13, 2022	Network Pruning	—Unverified
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation	Nov 30, 2019	Network Pruning	—Unverified
On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks	Jan 14, 2020	Network Pruning	—Unverified

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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