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

Showing 426450 of 534 papers

TitleStatusHype
Adaptive Pruning of Deep Neural Networks for Resource-Aware Embedded Intrusion Detection on the EdgeCode0
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary ParametersCode0
Progressive Stochastic Binarization of Deep NetworksCode0
GASL: Guided Attention for Sparsity Learning in Deep Neural NetworksCode0
The Other Side of Compression: Measuring Bias in Pruned TransformersCode0
Attention-Based Guided Structured Sparsity of Deep Neural NetworksCode0
Efficient Structured Pruning and Architecture Searching for Group ConvolutionCode0
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of MultipliersCode0
Learning Sparse Networks Using Targeted DropoutCode0
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group SparsityCode0
Less is KEN: a Universal and Simple Non-Parametric Pruning Algorithm for Large Language ModelsCode0
Rethinking Weight Decay For Efficient Neural Network PruningCode0
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter SamplerCode0
Pruning-aware Sparse Regularization for Network PruningCode0
Pruning by Explaining: A Novel Criterion for Deep Neural Network PruningCode0
LLM-Rank: A Graph Theoretical Approach to Pruning Large Language ModelsCode0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
The Search for Sparse, Robust Neural NetworksCode0
Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and MaskingCode0
Filter Pruning For CNN With Enhanced Linear Representation RedundancyCode0
Feature Selection for Multivariate Time Series via Network PruningCode0
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flightCode0
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural NetworksCode0
Self-supervised Feature-Gate Coupling for Dynamic Network PruningCode0
FastDepth: Fast Monocular Depth Estimation on Embedded SystemsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ResNet50-2.3 GFLOPsAccuracy78.79Unverified
2ResNet50-1.5 GFLOPsAccuracy78.07Unverified
3ResNet50 2.5 GFLOPSAccuracy78Unverified
4RegX-1.6GAccuracy77.97Unverified
5ResNet50 2.0 GFLOPSAccuracy77.7Unverified
6ResNet50-3G FLOPsAccuracy77.1Unverified
7ResNet50-2G FLOPsAccuracy76.4Unverified
8ResNet50-1G FLOPsAccuracy76.38Unverified
9TAS-pruned ResNet-50Accuracy76.2Unverified
10ResNet50Accuracy75.59Unverified
#ModelMetricClaimedVerifiedStatus
1FeatherTop-1 Accuracy76.93Unverified
2SpartanTop-1 Accuracy76.17Unverified
3ST-3Top-1 Accuracy76.03Unverified
4AC/DCTop-1 Accuracy75.64Unverified
5CSTop-1 Accuracy75.5Unverified
6ProbMaskTop-1 Accuracy74.68Unverified
7STRTop-1 Accuracy74.31Unverified
8DNWTop-1 Accuracy74Unverified
9GMPTop-1 Accuracy73.91Unverified
#ModelMetricClaimedVerifiedStatus
1+U-DML*Inference Time (ms)675.56Unverified
2DenseAccuracy79Unverified
3AC/DCAccuracy78.2Unverified
4Beta-RankAccuracy74.01Unverified
5TAS-pruned ResNet-110Accuracy73.16Unverified
#ModelMetricClaimedVerifiedStatus
1TAS-pruned ResNet-110Accuracy94.33Unverified
2ShuffleNet – QuantisedInference Time (ms)23.15Unverified
3AlexNet – QuantisedInference Time (ms)5.23Unverified
4MobileNet – QuantisedInference Time (ms)4.74Unverified
#ModelMetricClaimedVerifiedStatus
1FFN-ShapleyPrunedAvg #Steps12.05Unverified