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 351–375 of 534 papers

TitleStatusHype
Towards Compact and Robust Deep Neural Networks—0
Automated Channel Pruning with Learned Importance—0
NTP-INT: Network Traffic Prediction-Driven In-band Network Telemetry for High-load Switches—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Data-Independent Neural Pruning via Coresets—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Towards Fairness-aware Adversarial Network Pruning—0
One-shot Network Pruning at Initialization with Discriminative Image Patches—0
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluation—0
On Iterative Neural Network Pruning, Reinitialization, and the Similarity of Masks—0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
On the Decision Boundaries of Deep Neural Networks: A Tropical Geometry Perspective—0
On the Decision Boundaries of Neural Networks: A Tropical Geometry Perspective—0
On the Decision Boundaries of Neural Networks. A Tropical Geometry Perspective—0
On-the-fly Network Pruning for Object Detection—0
On the Landscape of Sparse Linear Networks—0
On the use of local structural properties for improving the efficiency of hierarchical community detection methods—0
Bypass Back-propagation: Optimization-based Structural Pruning for Large Language Models via Policy Gradient—0
Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate—0
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks—0
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning—0
Towards Higher Ranks via Adversarial Weight Pruning—0
A Unified Approach to Coreset Learning—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—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