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

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Showing 176–200 of 534 papers

TitleStatusHype
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
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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