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 126–150 of 534 papers

TitleStatusHype
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Cogradient Descent for Bilinear Optimization—0
Coarse and fine-grained automatic cropping deep convolutional neural network—0
Pruning coupled with learning, ensembles of minimal neural networks, and future of XAI—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration—0
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning—0
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN—0
Ensemble Mask Networks—0
Channel-wise pruning of neural networks with tapering resource constraint—0
Channel Planting for Deep Neural Networks using Knowledge Distillation—0
A relativistic extension of Hopfield neural networks via the mechanical analogy—0
Architecture-aware Network Pruning for Vision Quality Applications—0
Efficient Ensembles of Graph Neural Networks—0
Certified Invertibility in Neural Networks via Mixed-Integer Programming—0
AP: Selective Activation for De-sparsifying Pruned Neural Networks—0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—0
Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls for Network Pruning—0
CWP: Instance complexity weighted channel-wise soft masks for network pruning—0
Effective Subset Selection Through The Lens of Neural Network Pruning—0
CAP: Context-Aware Pruning for Semantic-Segmentation—0
CAP-Context-Aware-Pruning-for-Semantic-Segmentation—0
A Probabilistic Approach to Neural Network Pruning—0
Can We Find Strong Lottery Tickets in Generative Models?—0
ADMP: An Adversarial Double Masks Based Pruning Framework For Unsupervised Cross-Domain Compression—0
Show:102550
← PrevPage 6 of 22Next →
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