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
Sparse Double Descent: Where Network Pruning Aggravates OverfittingCode1
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches—0
Can pruning improve certified robustness of neural networks?Code0
Deep Neural Network Pruning for Nuclei Instance Segmentation in Hematoxylin & Eosin-Stained Histological Images—0
Neural Network Compression via Effective Filter Analysis and Hierarchical Pruning—0
Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm—0
Pruning for Feature-Preserving Circuits in CNNsCode0
Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural ReparameterizationCode1
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge—0
Spartan: Differentiable Sparsity via Regularized TransportationCode1
Pruning has a disparate impact on model accuracy—0
Quarantine: Sparsity Can Uncover the Trojan Attack Trigger for FreeCode1
Compression-aware Training of Neural Networks using Frank-WolfeCode0
Hyperparameter Optimization with Neural Network Pruning—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Neural Network Pruning by Cooperative Coevolution—0
APP: Anytime Progressive PruningCode1
Supervised Robustness-preserving Data-free Neural Network PruningCode0
AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack—0
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning—0
Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest FiltersCode1
Membership Inference Attacks and Defenses in Neural Network PruningCode1
PRUNIX: Non-Ideality Aware Convolutional Neural Network Pruning for Memristive Accelerators—0
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning—0
Pruning-aware Sparse Regularization for Network PruningCode0
Why Does DARTS Miss the Target, and How Do We Aim to Fix It?—0
Automatic Sparse Connectivity Learning for Neural Networks—0
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models—0
Neural Network Optimization for Reinforcement Learning Tasks Using Sparse Computations—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
Pruning deep neural networks generates a sparse, bio-inspired nonlinear controller for insect flightCode0
Distilling the Knowledge of Romanian BERTs Using Multiple TeachersCode0
Network Compression via Central FilterCode0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—0
Putting 3D Spatially Sparse Networks on a Diet—0
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory—0
DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networksCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Self-supervised Feature-Gate Coupling for Dynamic Network PruningCode0
Automatic Neural Network Pruning that Efficiently Preserves the Model AccuracyCode1
A Unified Approach to Coreset Learning—0
Neural network is heterogeneous: Phase matters moreCode0
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach—0
How I Learned to Stop Worrying and Love RetrainingCode0
RMNet: Equivalently Removing Residual Connection from NetworksCode1
RGP: Neural Network Pruning through Its Regular Graph Structure—0
When to Prune? A Policy towards Early Structural Pruning—0
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning—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