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 201–250 of 534 papers

TitleStatusHype
Model Compression Methods for YOLOv5: A Review—0
SqueezerFaceNet: Reducing a Small Face Recognition CNN Even More Via Filter Pruning—0
Neural Network Pruning as Spectrum Preserving Process—0
Distilled Pruning: Using Synthetic Data to Win the LotteryCode0
Structured Network Pruning by Measuring Filter-wise Interactions—0
Filter Pruning for Efficient CNNs via Knowledge-driven Differential Filter SamplerCode0
Low-Rank Prune-And-Factorize for Language Model Compression—0
Neural Network Pruning for Real-time Polyp Segmentation—0
Representation and decomposition of functions in DAG-DNNs and structural network pruning—0
Improving Generalization in Meta-Learning via Meta-Gradient AugmentationCode0
Resource Efficient Neural Networks Using Hessian Based Pruning—0
Does a sparse ReLU network training problem always admit an optimum?—0
Scaling Up Semi-supervised Learning with Unconstrained Unlabelled DataCode0
Layer-adaptive Structured Pruning Guided by Latency—0
Combining Multi-Objective Bayesian Optimization with Reinforcement Learning for TinyML—0
Probabilistic Modeling: Proving the Lottery Ticket Hypothesis in Spiking Neural Network—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
Network Pruning Spaces—0
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing—0
Beta-Rank: A Robust Convolutional Filter Pruning Method For Imbalanced Medical Image AnalysisCode0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
The Other Side of Compression: Measuring Bias in Pruned TransformersCode0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Improving the Transferability of Adversarial Examples via Direction TuningCode0
Protective Self-Adaptive Pruning to Better Compress DNNs—0
Differential Privacy Meets Neural Network Pruning—0
Knowledge-Enhanced Semi-Supervised Federated Learning for Aggregating Heterogeneous Lightweight Clients in IoTCode0
Parameter Sharing with Network Pruning for Scalable Multi-Agent Deep Reinforcement Learning—0
Fast as CHITA: Neural Network Pruning with Combinatorial Optimization—0
Less is More: The Influence of Pruning on the Explainability of CNNs—0
WHC: Weighted Hybrid Criterion for Filter Pruning on Convolutional Neural NetworksCode0
ST-MFNet Mini: Knowledge Distillation-Driven Frame InterpolationCode0
Adaptive Search-and-Training for Robust and Efficient Network PruningCode0
Certified Invertibility in Neural Networks via Mixed-Integer Programming—0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions—0
Pruning Compact ConvNets for Efficient Inference—0
Cross-Model Comparative Loss for Enhancing Neuronal Utility in Language UnderstandingCode0
Pruning Before Training May Improve Generalization, Provably—0
Towards Fairness-aware Adversarial Network Pruning—0
Structural Alignment for Network Pruning through Partial Regularization—0
Magnitude and Similarity based Variable Rate Filter Pruning for Efficient Convolution Neural NetworksCode0
Can We Find Strong Lottery Tickets in Generative Models?—0
AP: Selective Activation for De-sparsifying Pruned Neural Networks—0
Optimizing Learning Rate Schedules for Iterative Pruning of Deep Neural Networks—0
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning—0
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances—0
Distributed Pruning Towards Tiny Neural Networks in Federated Learning—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