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 276–300 of 534 papers

TitleStatusHype
Learning Compact Neural Networks with Regularization—0
Can We Find Strong Lottery Tickets in Generative Models?—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
Adaptive Consensus: A network pruning approach for decentralized optimization—0
Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach—0
Structured Pruning Meets Orthogonality—0
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning—0
Win the Lottery Ticket via Fourier Analysis: Frequencies Guided Network Pruning—0
Less is More: The Influence of Pruning on the Explainability of CNNs—0
Can network pruning benefit deep learning under label noise?—0
Linear Mode Connectivity in Sparse Neural Networks—0
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks—0
Structured Pruning of Recurrent Neural Networks through Neuron Selection—0
LNPT: Label-free Network Pruning and Training—0
Long Live the Lottery: The Existence of Winning Tickets in Lifelong Learning—0
Lost in Pruning: The Effects of Pruning Neural Networks beyond Test Accuracy—0
C2S2: Cost-aware Channel Sparse Selection for Progressive Network Pruning—0
Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networks—0
Low-Rank Prune-And-Factorize for Language Model Compression—0
When Are Neural Pruning Approximation Bounds Useful?—0
Block Pruning for Enhanced Efficiency in Convolutional Neural Networks—0
Blending Pruning Criteria for Convolutional Neural Networks—0
MARS: Multi-macro Architecture SRAM CIM-Based Accelerator with Co-designed Compressed Neural Networks—0
MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter 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