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 351–400 of 534 papers

TitleStatusHype
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning—0
Pruning Before Training May Improve Generalization, Provably—0
Three Dimensional Convolutional Neural Network Pruning with Regularization-Based Method—0
GD doesn't make the cut: Three ways that non-differentiability affects neural network training—0
New Pruning Method Based on DenseNet Network for Image Classification—0
To prune or not to prune : A chaos-causality approach to principled pruning of dense neural networks—0
Towards Communication-Learning Trade-off for Federated Learning at the Network Edge—0
Towards Compact and Robust Deep Neural Networks—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Towards Fairness-aware Adversarial Network Pruning—0
Towards Lightweight Graph Neural Network Search with Curriculum Graph Sparsification—0
Towards Lightweight Neural Animation : Exploration of Neural Network Pruning in Mixture of Experts-based Animation Models—0
Towards thinner convolutional neural networks through Gradually Global Pruning—0
Network Pruning via Annealing and Direct Sparsity Control—0
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design—0
Troubleshooting Blind Image Quality Models in the Wild—0
TSENOR: Highly-Efficient Algorithm for Finding Transposable N:M Sparse Masks—0
Ultrafast Photorealistic Style Transfer via Neural Architecture Search—0
Understanding Diversity Based Neural Network Pruning in Teacher Student Setup—0
"Understanding Robustness Lottery": A Geometric Visual Comparative Analysis of Neural Network Pruning Approaches—0
Unveiling Invariances via Neural Network Pruning—0
Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory—0
Variational Convolutional Neural Network Pruning—0
Verification of Neural Networks: Enhancing Scalability through Pruning—0
Waste not, Want not: All-Alive Pruning for Extremely Sparse Networks—0
Weight-dependent Gates for Network Pruning—0
Weight Reparametrization for Budget-Aware Network Pruning—0
When Are Neural Pruning Approximation Bounds Useful?—0
When to Prune? A Policy towards Early Structural Pruning—0
Why Does DARTS Miss the Target, and How Do We Aim to Fix It?—0
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning—0
Hyperflows: Pruning Reveals the Importance of Weights—0
Hyperparameter Optimization with Neural Network Pruning—0
Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum—0
Importance Estimation with Random Gradient for Neural Network Pruning—0
Improve Convolutional Neural Network Pruning by Maximizing Filter Variety—0
Iteratively Training Look-Up Tables for Network Quantization—0
Joint Regularization on Activations and Weights for Efficient Neural Network Pruning—0
Knowledge Distillation Circumvents Nonlinearity for Optical Convolutional Neural Networks—0
On the Landscape of One-hidden-layer Sparse Networks and Beyond—0
Layer-adaptive Structured Pruning Guided by Latency—0
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains—0
Learning ASR pathways: A sparse multilingual ASR model—0
Learning Compact Neural Networks with Regularization—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—0
LearningGroup: A Real-Time Sparse Training on FPGA via Learnable Weight Grouping for Multi-Agent Reinforcement Learning—0
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach—0
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning—0
Less is More: The Influence of Pruning on the Explainability of CNNs—0
Hierarchical Safety Realignment: Lightweight Restoration of Safety in Pruned Large Vision-Language ModelsCode0
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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