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 151–200 of 534 papers

TitleStatusHype
Hyperflows: Pruning Reveals the Importance of Weights—0
Can network pruning benefit deep learning under label noise?—0
Energy Consumption of Neural Networks on NVIDIA Edge Boards: an Empirical Model—0
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning—0
Enabling Image Recognition on Constrained Devices Using Neural Network Pruning and a CycleGAN—0
Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
Hyperparameter Optimization with Neural Network Pruning—0
LEAN: graph-based pruning for convolutional neural networks by extracting longest chains—0
A "Network Pruning Network" Approach to Deep Model Compression—0
Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning—0
Explicit Group Sparse Projection with Applications to Deep Learning and NMF—0
Bi-LSTM based Multi-Agent DRL with Computation-aware Pruning for Agent Twins Migration in Vehicular Embodied AI Networks—0
Efficient Ensembles of Graph Neural Networks—0
Effective Subset Selection Through The Lens of Neural Network Pruning—0
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
An End-to-End Network Pruning Pipeline with Sparsity Enforcement—0
GPU Acceleration of Sparse Neural Networks—0
Dynamic parameter reallocation improves trainability of deep convolutional networks—0
Accelerating Convolutional Neural Network Pruning via Spatial Aura Entropy—0
GraFT: Gradual Fusion Transformer for Multimodal Re-Identification—0
Hierarchical Action Classification with Network Pruning—0
Dynamic ASR Pathways: An Adaptive Masking Approach Towards Efficient Pruning of A Multilingual ASR Model—0
Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study—0
Dynamic Probabilistic Pruning: A general framework for hardware-constrained pruning at different granularities—0
Dynamical Isometry: The Missing Ingredient for Neural Network Pruning—0
Basis Scaling and Double Pruning for Efficient Inference in Network-Based Transfer Learning—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Fusion-Catalyzed Pruning for Optimizing Deep Learning on Intelligent Edge Devices—0
EdgeBERT: Sentence-Level Energy Optimizations for Latency-Aware Multi-Task NLP Inference—0
Diversity-boosted Generalization-Specialization Balancing for Zero-shot Learning—0
CCSRP: Robust Pruning of Spiking Neural Networks through Cooperative Coevolution—0
Does a sparse ReLU network training problem always admit an optimum?—0
Blending Pruning Criteria for Convolutional Neural Networks—0
Distortion-Aware Network Pruning and Feature Reuse for Real-time Video Segmentation—0
An Efficient Row-Based Sparse Fine-Tuning—0
GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization—0
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks—0
Efficient Stein Variational Inference for Reliable Distribution-lossless Network Pruning—0
Analysing Neural Network Topologies: a Game Theoretic Approach—0
Finding Stable Subnetworks at Initialization with Dataset Distillation—0
Pruning-Aware Merging for Efficient Multitask Inference—0
Discriminative Adversarial Unlearning—0
AutoPruning for Deep Neural Network with Dynamic Channel Masking—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
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—0
DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution—0
Show:102550
← PrevPage 4 of 11Next →
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