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 451–500 of 534 papers

TitleStatusHype
Waste not, Want not: All-Alive Pruning for Extremely Sparse Networks—0
Weight-dependent Gates for Network Pruning—0
Understanding the Effects of Data Parallelism and Sparsity on Neural Network Training—0
Connection Sensitivity Matters for Training-free DARTS: From Architecture-Level Scoring to Operation-Level Sensitivity Analysis—0
Differentiable Channel Sparsity Search via Weight Sharing within Filters—0
Differentiable Network Pruning for Microcontrollers—0
Differential Privacy Meets Neural Network Pruning—0
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Data Augmentations in Deep Weight Spaces—0
Discriminative Adversarial Unlearning—0
Pruning-Aware Merging for Efficient Multitask Inference—0
Self-Adaptive Network Pruning—0
Analysing Neural Network Topologies: a Game Theoretic Approach—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
Does a sparse ReLU network training problem always admit an optimum?—0
SequentialAttention++ for Block Sparsification: Differentiable Pruning Meets Combinatorial Optimization—0
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networks—0
A Main/Subsidiary Network Framework for Simplifying Binary Neural Network—0
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning—0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
dagger: A Python Framework for Reproducible Machine Learning Experiment Orchestration—0
Dynamic parameter reallocation improves trainability of deep convolutional networks—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Cross-Channel Intragroup Sparsity Neural Network—0
Crossbar-aware neural network pruning—0
Convolutional Neural Network Pruning with Structural Redundancy Reduction—0
Signal Collapse in One-Shot Pruning: When Sparse Models Fail to Distinguish Neural Representations—0
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—0
Convolutional Neural Network Pruning Using Filter Attenuation—0
Effective Subset Selection Through The Lens of Neural Network Pruning—0
Efficient Ensembles of Graph Neural Networks—0
A Lottery Ticket Hypothesis Framework for Low-Complexity Device-Robust Neural Acoustic Scene Classification—0
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning—0
Single-shot Channel Pruning Based on Alternating Direction Method of Multipliers—0
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning—0
Confident magnitude-based neural network pruning—0
Small Contributions, Small Networks: Efficient Neural Network Pruning Based on Relative Importance—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN—0
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
Enhanced Exploration in Neural Feature Selection for Deep Click-Through Rate Prediction Models via Ensemble of Gating Layers—0
Enhancing Scalability in Recommender Systems through Lottery Ticket Hypothesis and Knowledge Distillation-based Neural Network Pruning—0
Ensemble Mask Networks—0
EPSD: Early Pruning with Self-Distillation for Efficient Model Compression—0
SMOF: Squeezing More Out of Filters Yields Hardware-Friendly CNN Pruning—0
ExPAN(N)D: Exploring Posits for Efficient Artificial Neural Network Design in FPGA-based Systems—0
Improving Feature Attribution through Input-specific Network 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