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Title: GitHub - justrypython/Awesome-Pruning: A curated list of neural network pruning resources. · GitHub

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Type of Pruninghttps://github.com/justrypython/Awesome-Pruning#type-of-pruning
2020 Venueshttps://github.com/justrypython/Awesome-Pruning#2020
2019 Venueshttps://github.com/justrypython/Awesome-Pruning#2019
2018 Venueshttps://github.com/justrypython/Awesome-Pruning#2018
2017 Venueshttps://github.com/justrypython/Awesome-Pruning#2017
2016 Venueshttps://github.com/justrypython/Awesome-Pruning#2016
2015 Venueshttps://github.com/justrypython/Awesome-Pruning#2015
https://github.com/justrypython/Awesome-Pruning#type-of-pruning
https://github.com/justrypython/Awesome-Pruning#2020
EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruninghttps://arxiv.org/abs/2007.02491
PyTorch(Author)https://github.com/anonymous47823493/EagleEye
DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocationhttps://arxiv.org/abs/2004.02164
DHP: Differentiable Meta Pruning via HyperNetworkshttps://arxiv.org/abs/2003.13683
PyTorch(Author)https://github.com/ofsoundof/dhp
Meta-Learning with Network Pruninghttps://arxiv.org/abs/2007.03219
Accelerating CNN Training by Pruning Activation Gradientshttps://arxiv.org/abs/1908.00173
DA-NAS: Data Adapted Pruning for Efficient Neural Architecture Searchhttps://arxiv.org/abs/2003.12563
Differentiable Joint Pruning and Quantization for Hardware Efficiencyhttps://arxiv.org/abs/2007.10463
Channel Pruning via Automatic Structure Searchhttps://arxiv.org/abs/2001.08565
PyTorch(Author)https://github.com/lmbxmu/ABCPruner
Adversarial Neural Pruning with Latent Vulnerability Suppressionhttps://arxiv.org/abs/1908.04355
Proving the Lottery Ticket Hypothesis: Pruning is All You Needhttps://arxiv.org/abs/2002.00585
Soft Threshold Weight Reparameterization for Learnable Sparsityhttps://arxiv.org/abs/2002.03231
Pytorch(Author)https://github.com/RAIVNLab/STR
Network Pruning by Greedy Subnetwork Selectionhttps://arxiv.org/abs/2003.01794
Operation-Aware Soft Channel Pruning using Differentiable Maskshttps://arxiv.org/abs/2007.03938
DropNet: Reducing Neural Network Complexity via Iterative Pruninghttps://proceedings.icml.cc/static/paper_files/icml/2020/2026-Paper.pdf
Towards Efficient Model Compression via Learned Global Rankinghttps://arxiv.org/abs/1904.12368
Pytorch(Author)https://github.com/cmu-enyac/LeGR
HRank: Filter Pruning using High-Rank Feature Maphttps://arxiv.org/abs/2002.10179
Pytorch(Author)https://github.com/lmbxmu/HRank
Neural Network Pruning with Residual-Connections and Limited-Datahttps://arxiv.org/abs/1911.08114
Multi-Dimensional Pruning: A Unified Framework for Model Compressionhttp://openaccess.thecvf.com/content_CVPR_2020/html/Guo_Multi-Dimensional_Pruning_A_Unified_Framework_for_Model_Compression_CVPR_2020_paper.html
DMCP: Differentiable Markov Channel Pruning for Neural Networkshttps://arxiv.org/abs/2005.03354
TensorFlow(Author)https://github.com/zx55/dmcp
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compressionhttps://arxiv.org/abs/2003.08935
PyTorch(Author)https://github.com/ofsoundof/group_sparsity
Few Sample Knowledge Distillation for Efficient Network Compressionhttps://arxiv.org/abs/1812.01839
Discrete Model Compression With Resource Constraint for Deep Neural Networkshttp://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html
Structured Compression by Weight Encryption for Unstructured Pruning and Quantizationhttps://arxiv.org/abs/1905.10138
Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Accelerationhttp://openaccess.thecvf.com/content_CVPR_2020/html/He_Learning_Filter_Pruning_Criteria_for_Deep_Convolutional_Neural_Networks_Acceleration_CVPR_2020_paper.html
APQ: Joint Search for Network Architecture, Pruning and Quantization Policyhttps://arxiv.org/abs/2006.08509l
Comparing Rewinding and Fine-tuning in Neural Network Pruninghttps://arxiv.org/abs/2003.02389
TensorFlow(Author)https://github.com/lottery-ticket/rewinding-iclr20-public
A Signal Propagation Perspective for Pruning Neural Networks at Initializationhttps://arxiv.org/abs/1906.06307
ProxSGD: Training Structured Neural Networks under Regularization and Constraintshttps://openreview.net/forum?id=HygpthEtvr
TF+PT(Author)https://github.com/optyang/proxsgd
One-Shot Pruning of Recurrent Neural Networks by Jacobian Spectrum Evaluationhttps://arxiv.org/abs/1912.00120
Lookahead: A Far-sighted Alternative of Magnitude-based Pruninghttps://arxiv.org/abs/2002.04809
PyTorch(Author)https://github.com/alinlab/lookahead_pruning
Dynamic Model Pruning with Feedbackhttps://openreview.net/forum?id=SJem8lSFwB
Provable Filter Pruning for Efficient Neural Networkshttps://arxiv.org/abs/1911.07412
Data-Independent Neural Pruning via Coresetshttps://arxiv.org/abs/1907.04018
AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rateshttps://arxiv.org/abs/1907.03141
DARB: A Density-Aware Regular-Block Pruning for Deep Neural Networkshttp://arxiv.org/abs/1911.08020
Pruning from Scratchhttp://arxiv.org/abs/1909.12579
https://github.com/justrypython/Awesome-Pruning#2019
Network Pruning via Transformable Architecture Searchhttps://arxiv.org/abs/1905.09717
PyTorch(Author)https://github.com/D-X-Y/NAS-Projects
Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networkshttps://arxiv.org/abs/1909.08174
PyTorch(Author)https://github.com/youzhonghui/gate-decorator-pruning
Deconstructing Lottery Tickets: Zeros, Signs, and the Supermaskhttps://arxiv.org/abs/1905.01067
TensorFlow(Author)https://github.com/uber-research/deconstructing-lottery-tickets
One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizershttps://arxiv.org/abs/1906.02773
Global Sparse Momentum SGD for Pruning Very Deep Neural Networkshttps://arxiv.org/abs/1909.12778
PyTorch(Author)https://github.com/DingXiaoH/GSM-SGD
AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parametershttps://papers.nips.cc/paper/9521-autoprune-automatic-network-pruning-by-regularizing-auxiliary-parameters
Model Compression with Adversarial Robustness: A Unified Optimization Frameworkhttps://arxiv.org/abs/1902.03538
PyTorch(Author)https://github.com/TAMU-VITA/ATMC
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruninghttps://arxiv.org/abs/1903.10258
PyTorch(Author)https://github.com/liuzechun/MetaPruning
Accelerate CNN via Recursive Bayesian Pruninghttps://arxiv.org/abs/1812.00353
Adversarial Robustness vs Model Compression, or Both?https://arxiv.org/abs/1903.12561
PyTorch(Author)https://github.com/yeshaokai/Robustness-Aware-Pruning-ADMM
Learning Filter Basis for Convolutional Neural Network Compressionhttps://arxiv.org/abs/1908.08932
Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Accelerationhttps://arxiv.org/abs/1811.00250
PyTorch(Author)https://github.com/he-y/filter-pruning-geometric-median
Towards Optimal Structured CNN Pruning via Generative Adversarial Learninghttps://arxiv.org/abs/1903.09291
PyTorch(Author)https://github.com/ShaohuiLin/GAL
Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structurehttps://arxiv.org/abs/1904.03837
PyTorch(Author)https://github.com/ShawnDing1994/Centripetal-SGD
On Implicit Filter Level Sparsity in Convolutional Neural Networkshttps://arxiv.org/abs/1811.12495
Extension1https://arxiv.org/abs/1905.04967
Extension2https://openreview.net/forum?id=rylVvNS3hE
PyTorch(Author)https://github.com/mehtadushy/SelecSLS-Pytorch
Structured Pruning of Neural Networks with Budget-Aware Regularizationhttps://arxiv.org/abs/1811.09332
Importance Estimation for Neural Network Pruninghttp://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf
PyTorch(Author)https://github.com/NVlabs/Taylor_pruning
OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networkshttps://arxiv.org/abs/1905.11664
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Searchhttps://arxiv.org/abs/1903.03777
TensorFlow(Author)https://github.com/lixincn2015/Partial-Order-Pruning
Variational Convolutional Neural Network Pruninghttp://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Variational_Convolutional_Neural_Network_Pruning_CVPR_2019_paper.pdf
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networkshttps://arxiv.org/abs/1803.03635
TensorFlow(Author)https://github.com/google-research/lottery-ticket-hypothesis
Rethinking the Value of Network Pruninghttps://arxiv.org/abs/1810.05270
PyTorch(Author)https://github.com/Eric-mingjie/rethinking-network-pruning
Dynamic Channel Pruning: Feature Boosting and Suppressionhttps://arxiv.org/abs/1810.05331
TensorFlow(Author)https://github.com/deep-fry/mayo
SNIP: Single-shot Network Pruning based on Connection Sensitivityhttps://arxiv.org/abs/1810.02340
TensorFLow(Author)https://github.com/namhoonlee/snip-public
Dynamic Sparse Graph for Efficient Deep Learninghttps://arxiv.org/abs/1810.00859
CUDA(3rd)https://github.com/mtcrawshaw/dynamic-sparse-graph
Collaborative Channel Pruning for Deep Networkshttp://proceedings.mlr.press/v97/peng19c.html
Approximated Oracle Filter Pruning for Destructive CNN Width Optimization githubhttps://arxiv.org/abs/1905.04748
EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis4https://arxiv.org/abs/1905.05934
PyTorch(Author)https://github.com/alecwangcq/EigenDamage-Pytorch
https://github.com/justrypython/Awesome-Pruning#2018
Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layershttps://arxiv.org/abs/1802.00124
TensorFlow(Author)https://github.com/bobye/batchnorm_prune
PyTorch(3rd)https://github.com/jack-willturner/batchnorm-pruning
To prune, or not to prune: exploring the efficacy of pruning for model compressionhttps://arxiv.org/abs/1710.01878
Discrimination-aware Channel Pruning for Deep Neural Networkshttps://arxiv.org/abs/1810.11809
TensorFlow(Author)https://github.com/SCUT-AILab/DCP
Frequency-Domain Dynamic Pruning for Convolutional Neural Networkshttps://papers.NeurIPS.cc/paper/7382-frequency-domain-dynamic-pruning-for-convolutional-neural-networks.pdf
Learning Sparse Neural Networks via Sensitivity-Driven Regularizationhttps://arxiv.org/pdf/1810.11764.pdf
Amc: Automl for model compression and acceleration on mobile deviceshttps://arxiv.org/abs/1802.03494
TensorFlow(3rd)https://github.com/Tencent/PocketFlow#channel-pruning
Data-Driven Sparse Structure Selection for Deep Neural Networkshttps://arxiv.org/abs/1707.01213
MXNet(Author)https://github.com/TuSimple/sparse-structure-selection
Coreset-Based Neural Network Compressionhttps://arxiv.org/abs/1807.09810
PyTorch(Author)https://github.com/metro-smiles/CNN_Compression
Constraint-Aware Deep Neural Network Compressionhttp://www.sfu.ca/~ftung/papers/constraintaware_eccv18.pdf
SkimCaffe(Author)https://github.com/ChanganVR/ConstraintAwareCompression
A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliershttps://arxiv.org/abs/1804.03294
Caffe(Author)https://github.com/KaiqiZhang/admm-pruning
PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruninghttps://arxiv.org/abs/1711.05769
PyTorch(Author)https://github.com/arunmallya/packnet
NISP: Pruning Networks using Neuron Importance Score Propagationhttps://arxiv.org/abs/1711.05908
CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantizationhttp://www.sfu.ca/~ftung/papers/clipq_cvpr18.pdf
“Learning-Compression” Algorithms for Neural Net Pruninghttp://faculty.ucmerced.edu/mcarreira-perpinan/papers/cvpr18.pdf
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networkshttps://arxiv.org/abs/1808.06866
PyTorch(Author)https://github.com/he-y/soft-filter-pruning
Accelerating Convolutional Networks via Global & Dynamic Filter Pruninghttps://www.ijcai.org/proceedings/2018/0336.pdf
https://github.com/justrypython/Awesome-Pruning#2017
Pruning Filters for Efficient ConvNetshttps://arxiv.org/abs/1608.08710
PyTorch(3rd)https://github.com/Eric-mingjie/rethinking-network-pruning/tree/master/imagenet/l1-norm-pruning
Pruning Convolutional Neural Networks for Resource Efficient Inferencehttps://arxiv.org/abs/1611.06440
TensorFlow(3rd)https://github.com/Tencent/PocketFlow#channel-pruning
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guaranteehttps://arxiv.org/abs/1611.05162
TensorFlow(Author)https://github.com/DNNToolBox/Net-Trim-v1
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeonhttps://arxiv.org/abs/1705.07565
PyTorch(Author)https://github.com/csyhhu/L-OBS
Runtime Neural Pruninghttps://papers.NeurIPS.cc/paper/6813-runtime-neural-pruning
Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruninghttps://arxiv.org/abs/1611.05128
ThiNet: A Filter Level Pruning Method for Deep Neural Network Compressionhttps://arxiv.org/abs/1707.06342
Caffe(Author)https://github.com/Roll920/ThiNet
PyTorch(3rd)https://github.com/tranorrepository/reprod-thinet
Channel pruning for accelerating very deep neural networkshttps://arxiv.org/abs/1707.06168
Caffe(Author)https://github.com/yihui-he/channel-pruning
Learning Efficient Convolutional Networks Through Network Slimminghttps://arxiv.org/abs/1708.06519
PyTorch(Author)https://github.com/Eric-mingjie/network-slimming
https://github.com/justrypython/Awesome-Pruning#2016
Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Codinghttps://arxiv.org/abs/1510.00149
Caffe(Author)https://github.com/songhan/Deep-Compression-AlexNet
Dynamic Network Surgery for Efficient DNNshttps://arxiv.org/abs/1608.04493
Caffe(Author)https://github.com/yiwenguo/Dynamic-Network-Surgery
https://github.com/justrypython/Awesome-Pruning#2015
Learning both Weights and Connections for Efficient Neural Networkshttps://arxiv.org/abs/1506.02626
PyTorch(3rd)https://github.com/jack-willturner/DeepCompression-PyTorch
https://github.com/justrypython/Awesome-Pruning#related-repo
Awesome-model-compression-and-accelerationhttps://github.com/memoiry/Awesome-model-compression-and-acceleration
EfficientDNNshttps://github.com/MingSun-Tse/EfficientDNNs
Embedded-Neural-Networkhttps://github.com/ZhishengWang/Embedded-Neural-Network
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