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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 501–534 of 534 papers

TitleStatusHype
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Exploring Neural Network Pruning with Screening Methods—0
Extract Local Inference Chains of Deep Neural Nets—0
“Learning-Compression” Algorithms for Neural Net Pruning—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
Softer Pruning, Incremental Regularization—0
A Greedy Hierarchical Approach to Whole-Network Filter-Pruning in CNNs—0
Comprehensive Online Network Pruning via Learnable Scaling Factors—0
Aggressive Post-Training Compression on Extremely Large Language Models—0
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
FedMef: Towards Memory-efficient Federated Dynamic Pruning—0
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity—0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning—0
A flexible, extensible software framework for model compression based on the LC algorithm—0
SparseRT: Accelerating Unstructured Sparsity on GPUs for Deep Learning Inference—0
Composition of Saliency Metrics for Channel Pruning with a Myopic Oracle—0
Complexity-Driven CNN Compression for Resource-constrained Edge AI—0
Finding Deep Local Optima Using Network Pruning—0
Complexity-Aware Training of Deep Neural Networks for Optimal Structure Discovery—0
Compact Neural Representation Using Attentive Network Pruning—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization—0
Compact Deep Convolutional Neural Networks With Coarse Pruning—0
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices—0
A Feature-map Discriminant Perspective for Pruning Deep Neural Networks—0
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization—0
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions—0
Adversarial Robustness of Distilled and Pruned Deep Learning-based Wireless Classifiers—0
Compact CNN Models for On-device Ocular-based User Recognition in Mobile Devices—0
Cogradient Descent for Bilinear Optimization—0
GPU Acceleration of Sparse Neural Networks—0
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification—0
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images—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