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 501–534 of 534 papers

TitleStatusHype
Clustering Convolutional Kernels to Compress Deep Neural NetworksCode0
On-Device Neural Language Model Based Word PredictionCode0
Crossbar-aware neural network pruning—0
Fast Convex Pruning of Deep Neural NetworksCode0
“Learning-Compression” Algorithms for Neural Net Pruning—0
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization—0
Channel Gating Neural NetworksCode1
Structured Pruning for Efficient ConvNets via Incremental RegularizationCode0
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of MultipliersCode0
A Quantization-Friendly Separable Convolution for MobileNetsCode0
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural NetworksCode1
Learning Sparse Structured Ensembles with SG-MCMC and Network Pruning—0
Building Efficient ConvNets using Redundant Feature PruningCode0
Efficient Sparse-Winograd Convolutional Neural NetworksCode0
Attention-Based Guided Structured Sparsity of Deep Neural NetworksCode0
Learning Compact Neural Networks with Regularization—0
Recent Advances in Efficient Computation of Deep Convolutional Neural Networks—0
A relativistic extension of Hopfield neural networks via the mechanical analogy—0
Structured Deep Neural Network Pruning via Matrix Pivoting—0
NISP: Pruning Networks using Neuron Importance Score Propagation—0
PackNet: Adding Multiple Tasks to a Single Network by Iterative PruningCode1
NeST: A Neural Network Synthesis Tool Based on a Grow-and-Prune Paradigm—0
TraNNsformer: Neural network transformation for memristive crossbar based neuromorphic system design—0
Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization—0
SCNN: An Accelerator for Compressed-sparse Convolutional Neural Networks—0
Towards thinner convolutional neural networks through Gradually Global Pruning—0
The Incredible Shrinking Neural Network: New Perspectives on Learning Representations Through The Lens of Pruning—0
Compact Deep Convolutional Neural Networks With Coarse Pruning—0
Pruning Filters for Efficient ConvNetsCode2
Reducing the Model Order of Deep Neural Networks Using Information Theory—0
On-the-fly Network Pruning for Object Detection—0
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model sizeCode1
Structured Pruning of Deep Convolutional Neural NetworksCode0
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman CodingCode0
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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