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 376–400 of 534 papers

TitleStatusHype
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
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
Linear Mode Connectivity in Sparse Neural Networks—0
Lipschitz Constant Meets Condition Number: Learning Robust and Compact Deep Neural Networks—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
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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