SOTAVerified

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

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Showing 151–175 of 534 papers

TitleStatusHype
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning—0
Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity—0
GPU Acceleration of Sparse Neural Networks—0
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Blending Pruning Criteria for Convolutional Neural Networks—0
Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks—0
Extract Local Inference Chains of Deep Neural Nets—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
An End-to-End Network Pruning Pipeline with Sparsity Enforcement—0
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
Improving Feature Attribution through Input-specific Network Pruning—0
Exploring Neural Network Pruning with Screening Methods—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study—0
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning—0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Ensemble Mask Networks—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution—0
Does a sparse ReLU network training problem always admit an optimum?—0
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All datasetsImageNetImageNet - ResNet 50 - 90% sparsityCIFAR-100CIFAR-10MNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50-2.3 GFLOPsAccuracy78.79—Unverified
2ResNet50-1.5 GFLOPsAccuracy78.07—Unverified
3ResNet50 2.5 GFLOPSAccuracy78—Unverified
4RegX-1.6GAccuracy77.97—Unverified
5ResNet50 2.0 GFLOPSAccuracy77.7—Unverified
6ResNet50-3G FLOPsAccuracy77.1—Unverified
7ResNet50-2G FLOPsAccuracy76.4—Unverified
8ResNet50-1G FLOPsAccuracy76.38—Unverified
9TAS-pruned ResNet-50Accuracy76.2—Unverified
10ResNet50Accuracy75.59—Unverified
#ModelMetricClaimedVerifiedStatus
1FeatherTop-1 Accuracy76.93—Unverified
2SpartanTop-1 Accuracy76.17—Unverified
3ST-3Top-1 Accuracy76.03—Unverified
4AC/DCTop-1 Accuracy75.64—Unverified
5CSTop-1 Accuracy75.5—Unverified
6ProbMaskTop-1 Accuracy74.68—Unverified
7STRTop-1 Accuracy74.31—Unverified
8DNWTop-1 Accuracy74—Unverified
9GMPTop-1 Accuracy73.91—Unverified
#ModelMetricClaimedVerifiedStatus
1+U-DML*Inference Time (ms)675.56—Unverified
2DenseAccuracy79—Unverified
3AC/DCAccuracy78.2—Unverified
4Beta-RankAccuracy74.01—Unverified
5TAS-pruned ResNet-110Accuracy73.16—Unverified
#ModelMetricClaimedVerifiedStatus
1TAS-pruned ResNet-110Accuracy94.33—Unverified
2ShuffleNet – QuantisedInference Time (ms)23.15—Unverified
3AlexNet – QuantisedInference Time (ms)5.23—Unverified
4MobileNet – QuantisedInference Time (ms)4.74—Unverified
#ModelMetricClaimedVerifiedStatus
1FFN-ShapleyPrunedAvg #Steps12.05—Unverified