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 201–225 of 534 papers

TitleStatusHype
ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-based Systems—0
Improving Feature Attribution through Input-specific Network Pruning—0
Importance Estimation with Random Gradient for Neural Network Pruning—0
Analysing Neural Network Topologies: a Game Theoretic Approach—0
Extract Local Inference Chains of Deep Neural Nets—0
CAP: Context-Aware Pruning for Semantic-Segmentation—0
Pruning-Aware Merging for Efficient Multitask Inference—0
Discriminative Adversarial Unlearning—0
Cascade Weight Shedding in Deep Neural Networks: Benefits and Pitfalls for Network Pruning—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—0
FedMef: Towards Memory-efficient Federated Dynamic Pruning—0
FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning—0
Automatic Sparse Connectivity Learning for Neural Networks—0
Differential Privacy Meets Neural Network Pruning—0
Adaptive Neural Connections for Sparsity Learning—0
Finding Deep Local Optima Using Network Pruning—0
Hyperflows: Pruning Reveals the Importance of Weights—0
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety—0
Layer-adaptive Structured Pruning Guided by Latency—0
Differentiable Network Pruning for Microcontrollers—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—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