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 150 of 534 papers

TitleStatusHype
Exploring GLU Expansion Ratios: A Study of Structured Pruning in LLaMA-3.2 ModelsCode5
DepGraph: Towards Any Structural PruningCode4
Structured Pruning for Deep Convolutional Neural Networks: A surveyCode4
Pruning Filters for Efficient ConvNetsCode2
LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPSCode2
A Simple and Effective Pruning Approach for Large Language ModelsCode2
MaskLLM: Learnable Semi-Structured Sparsity for Large Language ModelsCode2
A Survey on Deep Neural Network Pruning-Taxonomy, Comparison, Analysis, and RecommendationsCode2
Is C4 Dataset Optimal for Pruning? An Investigation of Calibration Data for LLM PruningCode1
Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement LearningCode1
Group Fisher Pruning for Practical Network CompressionCode1
Good Subnetworks Provably Exist: Pruning via Greedy Forward SelectionCode1
How Not to Give a FLOP: Combining Regularization and Pruning for Efficient InferenceCode1
Investigating Sparsity in Recurrent Neural NetworksCode1
Iterative Soft Shrinkage Learning for Efficient Image Super-ResolutionCode1
Filter Sketch for Network PruningCode1
Effective Sparsification of Neural Networks with Global Sparsity ConstraintCode1
Recent Advances on Neural Network Pruning at InitializationCode1
Finding Lottery Tickets in Vision Models via Data-driven Spectral Foresight PruningCode1
EfficientTDNN: Efficient Architecture Search for Speaker RecognitionCode1
Feather: An Elegant Solution to Effective DNN SparsificationCode1
Filter-Pruning of Lightweight Face Detectors Using a Geometric Median CriterionCode1
Accelerating Sparse DNN Models without Hardware-Support via Tile-Wise SparsityCode1
Fluctuation-based Adaptive Structured Pruning for Large Language ModelsCode1
1xN Pattern for Pruning Convolutional Neural NetworksCode1
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial RobustnessCode1
AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural NetworksCode1
A Three-regime Model of Network PruningCode1
How Sparse Can We Prune A Deep Network: A Fundamental Limit ViewpointCode1
HRank: Filter Pruning using High-Rank Feature MapCode1
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networksCode1
Comparing Rewinding and Fine-tuning in Neural Network PruningCode1
DHP: Differentiable Meta Pruning via HyperNetworksCode1
Dynamic Channel Pruning: Feature Boosting and SuppressionCode1
A Gradient Flow Framework For Analyzing Network PruningCode1
Auto-Train-Once: Controller Network Guided Automatic Network Pruning from ScratchCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Discrimination-aware Network Pruning for Deep Model CompressionCode1
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With Trainable Masked LayersCode1
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network PruningCode1
Efficient Latency-Aware CNN Depth Compression via Two-Stage Dynamic ProgrammingCode1
M-FAC: Efficient Matrix-Free Approximations of Second-Order InformationCode1
Beyond Size: How Gradients Shape Pruning Decisions in Large Language ModelsCode1
Automatic Network Pruning via Hilbert-Schmidt Independence Criterion Lasso under Information Bottleneck PrincipleCode1
Layer-adaptive sparsity for the Magnitude-based PruningCode1
An Information Theory-inspired Strategy for Automatic Network PruningCode1
Discovering Neural WiringsCode1
APP: Anytime Progressive PruningCode1
Advanced Dropout: A Model-free Methodology for Bayesian Dropout OptimizationCode1
Automatic Neural Network Pruning that Efficiently Preserves the Model AccuracyCode1
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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