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 176–200 of 534 papers

TitleStatusHype
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
An End-to-End Network Pruning Pipeline with Sparsity Enforcement—0
Towards Higher Ranks via Adversarial Weight Pruning—0
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning—0
Neural Network Pruning by Gradient DescentCode0
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks—0
Do Localization Methods Actually Localize Memorized Data in LLMs? A Tale of Two BenchmarksCode0
Data Augmentations in Deep Weight Spaces—0
Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks—0
Efficient Model-Based Deep Learning via Network Pruning and Fine-TuningCode0
Importance Estimation with Random Gradient for Neural Network Pruning—0
SparseByteNN: A Novel Mobile Inference Acceleration Framework Based on Fine-Grained Group SparsityCode0
Linear Mode Connectivity in Sparse Neural Networks—0
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification—0
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks—0
Filter Pruning For CNN With Enhanced Linear Representation RedundancyCode0
SWAP: Sparse Entropic Wasserstein Regression for Robust Network PruningCode0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
Unveiling Invariances via Neural Network Pruning—0
Ensemble Mask Networks—0
EDAC: Efficient Deployment of Audio Classification Models For COVID-19 DetectionCode0
Adaptive Consensus: A network pruning approach for decentralized optimization—0
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks—0
Pruning a neural network using Bayesian inference—0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—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