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section_mapping = {
"2.1.1": ("Weight-Dependent", "Filter Norm"),
"2.1.2": ("Weight-Dependent", "Filter Correlation"),
"2.2.1": ("Activation-Based", "Current Layer"),
"2.2.2": ("Activation-Based", "Adjacent Layer"),
"2.2.3": ("Activation-Based", "All Layer"),
"2.3.1": ("Regularization", "on BN Parameters"),
"2.3.2": ("Regularization", "on Extra Parameters"),
"2.3.3": ("Regularization", "on Filters"),
"2.4.1": ("Optimization Tools", "Taylor Expansion"),
"2.4.2": ("Optimization Tools", "Variational Bayesian"),
"2.4.3": ("Optimization Tools", "Others"),
"2.5.1": ("Dynamic Pruning", "Dynamic during Training"),
"2.5.2": ("Dynamic Pruning", "Dynamic during Inference"),
"2.6.1": ("Neural Architecture Search", "Reinforcement Learning-Based"),
"2.6.2": ("Neural Architecture Search", "Gradient-Based"),
"2.6.3": ("Neural Architecture Search", "Evolutionary-Based"),
"2.7.1": ("Extensions", "Lottery Ticket Hypothesis"),
"2.7.2": ("Extensions", "Joint Compression"),
"2.7.3": ("Extensions", "Special Granularity"),
}
paper_mapping = {
"PFEC": "| [Pruning Filters for Efficient ConvNets](https://arxiv.org/abs/1608.08710) | ICLR | `F` | [PyTorch(3rd)](https://github.com/Eric-mingjie/rethinking-network-pruning/tree/master/imagenet/l1-norm-pruning) |",
"FPGM": "| [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://arxiv.org/abs/1811.00250) | CVPR **(Oral)** | `F` | [PyTorch(Author)](https://github.com/he-y/filter-pruning-geometric-median) |",
"RED": "| [RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks](https://papers.nips.cc/paper/2021/hash/ae5e3ce40e0404a45ecacaaf05e5f735-Abstract.html) | NeurIPS | `F` | - |",
"RED++": "| [RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging](https://arxiv.org/abs/2110.01397) | TPAMI |||",
"COP": "| [COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning](https://arxiv.org/abs/1906.10337) | IJCAI | `F` | [Tensorflow(Author)](https://github.com/ZJULearning/COP) |",
"SRR": "| [Convolutional Neural Network Pruning with Structural Redundancy Reduction](https://arxiv.org/abs/2104.03438) | CVPR | `F` | - |",
"CLR-RNF": "| [Pruning Networks with Cross-Layer Ranking & k-Reciprocal Nearest Filters](https://arxiv.org/pdf/2202.07190.pdf) | TNNLS |||",
"EPruner": "| [Network Pruning using Adaptive Exemplar Filters](https://arxiv.org/pdf/2101.07985.pdf) | TNNLS ||[PyTorch(Author)](https://github.com/lmbxmu/EPruner)|",
"CP": "| [Channel pruning for accelerating very deep neural networks](https://arxiv.org/abs/1707.06168) | ICCV | `F` | [Caffe(Author)](https://github.com/yihui-he/channel-pruning) |",
"HRank": "| [HRank: Filter Pruning using High-Rank Feature Map](https://arxiv.org/abs/2002.10179) | CVPR **(Oral)** | `F` | [Pytorch(Author)](https://github.com/lmbxmu/HRank) |",
"CBC": "| [Coreset-Based Neural Network Compression](https://arxiv.org/abs/1807.09810) | ECCV | `F` | [PyTorch(Author)](https://github.com/metro-smiles/CNN_Compression) |",
"CHIP": "| [CHIP: CHannel Independence-based Pruning for Compact Neural Networks](https://papers.nips.cc/paper/2021/hash/ce6babd060aa46c61a5777902cca78af-Abstract.html) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/Eclipsess/CHIP_NeurIPS2021) |",
"APoZ": "| [Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures](https://arxiv.org/abs/1607.03250) | Arxiv |||",
"DropNet": "DropNet: Reducing Neural Network Complexity via Iterative Pruning](https://proceedings.mlr.press/v119/tan20a.html) | ICML | `F` | - |",
"LRMF": "| [Filter pruning via learned representation median in the frequency domain](https://ieeexplore.ieee.org/document/9622118) | IEEE Trans. Cybern. |||",
"GCNP": "| [On the channel pruning using graph convolution network for convolutional neural network acceleration](https://www.ijcai.org/proceedings/2022/0431.pdf) | IJCAI |||",
"ThiNet": "| [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://arxiv.org/abs/1707.06342) | ICCV | `F` | [Caffe(Author)](https://github.com/Roll920/ThiNet), [PyTorch(3rd)](https://github.com/tranorrepository/reprod-thinet) |",
"AOFP": "| [Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github](https://arxiv.org/abs/1905.04748) | ICML | `F` | - |",
"GFS": "| [Good Subnetworks Provably Exist: Pruning via Greedy Forward Selection](https://proceedings.mlr.press/v119/ye20b/ye20b.pdf)| ICML | `F` ||",
"NISP": "| [NISP: Pruning Networks using Neuron Importance Score Propagation](https://arxiv.org/abs/1711.05908) | CVPR | `F` | - |",
"DCP": "| [Discrimination-aware Channel Pruning for Deep Neural Networks](https://arxiv.org/abs/1810.11809) | NeurIPS | `F` | [TensorFlow(Author)](https://github.com/SCUT-AILab/DCP) |",
"PFP": "| [Provable Filter Pruning for Efficient Neural Networks](https://arxiv.org/abs/1911.07412) | ICLR | `F` | - |",
"DLRFC": "| [Filter Pruning via Feature Discrimination in Deep Neural Networks](https://link.springer.com/10.1007/978-3-031-19803-8_15) | ECCV | `F` | - |",
"NS": "| [Learning Efficient Convolutional Networks Through Network Slimming](https://arxiv.org/abs/1708.06519) | ICCV | `F` | [PyTorch(Author)](https://github.com/Eric-mingjie/network-slimming) |",
"GBN": "| [Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks](https://arxiv.org/abs/1909.08174) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/youzhonghui/gate-decorator-pruning) |",
"PR": "| [Neuron-level Structured Pruning using Polarization Regularizer](https://papers.nips.cc/paper/2020/file/703957b6dd9e3a7980e040bee50ded65-Paper.pdf) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/polarizationpruning/PolarizationPruning) |",
"RSNLI": "| [Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers](https://arxiv.org/abs/1802.00124) | ICLR | `F` | [TensorFlow(Author)](https://github.com/bobye/batchnorm_prune), [PyTorch(3rd)](https://github.com/jack-willturner/batchnorm-pruning) |",
"SCP": "| [Operation-Aware Soft Channel Pruning using Differentiable Masks](https://arxiv.org/abs/2007.03938) | ICML | `F` | - |",
"EagleEye": "| [EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning](https://arxiv.org/abs/2007.02491) | ECCV **(Oral)** | `F` | [PyTorch(Author)](https://github.com/anonymous47823493/EagleEye) |",
"SSS": "| [Data-Driven Sparse Structure Selection for Deep Neural Networks](https://arxiv.org/abs/1707.01213) | ECCV | `F` | [MXNet(Author)](https://github.com/TuSimple/sparse-structure-selection) |",
"GAL": "| [Towards Optimal Structured CNN Pruning via Generative Adversarial Learning](https://arxiv.org/abs/1903.09291) | CVPR | `F` | [PyTorch(Author)](https://github.com/ShaohuiLin/GAL) |",
"DMC": "| [Discrete Model Compression With Resource Constraint for Deep Neural Networks](http://openaccess.thecvf.com/content_CVPR_2020/html/Gao_Discrete_Model_Compression_With_Resource_Constraint_for_Deep_Neural_Networks_CVPR_2020_paper.html) | CVPR | `F` | - |",
"GDP-Guo": "| [GDP: Stabilized Neural Network Pruning via Gates with Differentiable Polarization](https://openaccess.thecvf.com/content/ICCV2021/html/Guo_GDP_Stabilized_Neural_Network_Pruning_via_Gates_With_Differentiable_Polarization_ICCV_2021_paper.html) | ICCV | `F` | - |",
"ResRep": "| [ResRep: Lossless CNN Pruning via Decoupling Remembering and Forgetting](https://openaccess.thecvf.com/content/ICCV2021/html/Ding_ResRep_Lossless_CNN_Pruning_via_Decoupling_Remembering_and_Forgetting_ICCV_2021_paper.html) | ICCV | `F` | [PyTorch(Author)](https://github.com/DingXiaoH/ResRep) |",
"SCOP": "| [SCOP: Scientific Control for Reliable Neural Network Pruning](https://arxiv.org/abs/2010.10732) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/yehuitang/Pruning/tree/master/SCOP_NeurIPS2020) |",
"BAR": "| [Structured Pruning of Neural Networks with Budget-Aware Regularization](https://arxiv.org/abs/1811.09332) | CVPR | `F` | - |",
"ABP": "| [Adding before pruning: Sparse filter fusion for deep convolutional neural networks via auxiliary attention](https://ieeexplore.ieee.org/abstract/document/9530256) |TNNLS|||",
"WhiteBox": "|[Carrying out cnn channel pruning in a white box](https://arxiv.org/pdf/2104.11883.pdf) | TNNLS |||",
"LeGR": "| [Towards Efficient Model Compression via Learned Global Ranking](https://arxiv.org/abs/1904.12368) | CVPR **(Oral)** | `F` | [Pytorch(Author)](https://github.com/cmu-enyac/LeGR) |",
"ML1R": "| [Learning optimized structure of neural networks by hidden node pruning with $l 1$ regularization](https://ieeexplore.ieee.org/abstract/document/8911376) |IEEE Trans. Cybern. |||",
"SSL": "| [Learning Structured Sparsity in Deep Neural Networks](https://papers.nips.cc/paper_files/paper/2016/file/41bfd20a38bb1b0bec75acf0845530a7-Paper.pdf) | NeurIPS | `F` |[PyTorch(3rd)](https://github.com/ZJCV/SSL)|",
"OICSR": "| [OICSR: Out-In-Channel Sparsity Regularization for Compact Deep Neural Networks](https://arxiv.org/abs/1905.11664) | CVPR | `F` | - |",
"OTO": "| [Only Train Once: A One-Shot Neural Network Training And Pruning Framework](https://papers.nips.cc/paper/2021/hash/a376033f78e144f494bfc743c0be3330-Abstract.html) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/tianyic/onlytrainonce) |",
"GREG": "| [Neural Pruning via Growing Regularization](https://openreview.net/forum?id=o966_Is_nPA) | ICLR | `F` | [PyTorch(Author)](https://github.com/MingSun-Tse/Regularization-Pruning) |",
"Mol-16": "| [Pruning Convolutional Neural Networks for Resource Efficient Inference](https://arxiv.org/abs/1611.06440) | ICLR | `F` | [TensorFlow(3rd)](https://github.com/Tencent/PocketFlow#channel-pruning) |",
"Mol-19": "| [Importance Estimation for Neural Network Pruning](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) | CVPR | `F` | [PyTorch(Author)](https://github.com/NVlabs/Taylor_pruning) |",
"CCP": "| [Collaborative Channel Pruning for Deep Networks](http://proceedings.mlr.press/v97/peng19c.html) | ICML | `F` | - |",
"ED": "| [EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis](https://arxiv.org/abs/1905.05934) | ICML | `F` | [PyTorch(Author)](https://github.com/alecwangcq/EigenDamage-Pytorch) |",
"GFP": "| [Group Fisher Pruning for Practical Network Compression](https://arxiv.org/abs/2108.00708) | ICML | `F` | [PyTorch(Author)](https://github.com/jshilong/FisherPruning) |",
"HAP": "| [Hessian-Aware Pruning and Optimal Neural Implant](https://openaccess.thecvf.com/content/WACV2022/papers/Yu_Hessian-Aware_Pruning_and_Optimal_Neural_Implant_WACV_2022_paper.pdf) | WACV | `F` | [PyTorch(Author)](https://github.com/yaozhewei/HAP)|",
"SOSP": "| [SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning](https://openreview.net/forum?id=t5EmXZ3ZLR) | ICLR **(Spotlight)** | `F` | [PyTorch(Author)](https://github.com/boschresearch/sosp)(Releasing) |",
"VP": "| [Variational Convolutional Neural Network Pruning](https://openaccess.thecvf.com/content_CVPR_2019/html/Zhao_Variational_Convolutional_Neural_Network_Pruning_CVPR_2019_paper.html) | CVPR | `F` | - |",
"RBP": "| [Accelerate CNN via Recursive Bayesian Pruning](https://arxiv.org/abs/1812.00353) | ICCV | `F` | - |",
"VIBNet": "| [Compressing Neural Networks using the Variational Information Bottleneck](https://proceedings.mlr.press/v80/dai18d.html) | ICML | `F` | [PyTorch(Author)](https://github.com/zhuchen03/VIBNet) |",
"Horseshoe": "| [Bayesian Compression for Deep Learning](https://proceedings.neurips.cc/paper/2017/hash/69d1fc78dbda242c43ad6590368912d4-Abstract.html) | NeurIPS | `F` | - |",
"Log-normal": "| [Structured Bayesian Pruning via Log-Normal Multiplicative Noise](https://papers.nips.cc/paper/2017/hash/dab49080d80c724aad5ebf158d63df41-Abstract.html) | NeurIPS | `F` | - |",
"C-SGD": "| [Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure](https://arxiv.org/abs/1904.03837) | CVPR | `F` | [PyTorch(Author)](https://github.com/ShawnDing1994/Centripetal-SGD) |",
"RMDA": "| [Training Structured Neural Networks Through Manifold Identification and Variance Reduction](https://openreview.net/forum?id=mdUYT5QV0O) | ICLR | `F` | [PyTorch(Author)](https://www.github.com/zihsyuan1214/rmda) |",
"Mehta-19": "| [On Implicit Filter Level Sparsity in Convolutional Neural Networks](https://arxiv.org/abs/1811.12495), [Extension1](https://arxiv.org/abs/1905.04967), [Extension2](https://openreview.net/forum?id=rylVvNS3hE) | CVPR | `F` | [PyTorch(Author)](https://github.com/mehtadushy/SelecSLS-Pytorch) |",
"EKG": "| [Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning](https://link.springer.com/10.1007/978-3-031-20083-0_34) | ECCV | `F` | [PyTorch(Author)](https://github.com/sseung0703/EKG) |",
"StructADMM": "| [Structadmm: Achieving ultrahigh efficiency in structured pruning for dnns](https://ieeexplore.ieee.org/abstract/document/9354506) |TNNLS|||",
"Non-structured-ADMM": "| [Non-structured dnn weight pruning鈥攊s it beneficial in any platform](https://ieeexplore.ieee.org/abstract/document/9381660) |TNNLS|||",
"RollBack": "| [Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning](https://link.springer.com/10.1007/978-3-031-20050-2_29) | ECCV | `F` | [PyTorch(Author)](https://github.com/fanhanwei/BOCR) |",
"ST": "| [A Unified Framework for Soft Threshold Pruning](https://openreview.net/forum?id=cCFqcrq0d8) | ICLR | `W` | [PyTorch(Author)](https://github.com/Yanqi-Chen/LATS) |",
"SFP": "| [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://arxiv.org/abs/1808.06866) | IJCAI | `F` | [PyTorch(Author)](https://github.com/he-y/soft-filter-pruning) |",
"GDP-Lin": "| [Accelerating Convolutional Networks via Global & Dynamic Filter Pruning](https://www.ijcai.org/proceedings/2018/0336.pdf) | IJCAI | `F` | - |",
"DPF": "| [Dynamic Model Pruning with Feedback](https://openreview.net/forum?id=SJem8lSFwB) | ICLR | `WF` | - |",
"CHEX": "| [CHEX: CHannel EXploration for CNN Model Compression](https://openaccess.thecvf.com/content/CVPR2022/html/Hou_CHEX_CHannel_EXploration_for_CNN_Model_Compression_CVPR_2022_paper.html) | CVPR | `F` | - |",
"DSG": "| [Dynamic Sparse Graph for Efficient Deep Learning](https://arxiv.org/abs/1810.00859) | ICLR | `F` | [CUDA(3rd)](https://github.com/mtcrawshaw/dynamic-sparse-graph) |",
"SEP": "| [Where to prune: Using lstm to guide data-dependent soft pruning](https://pure.aber.ac.uk/portal/files/39765364/Where_to_Prune_Using_LSTM_to_Guide_Data_dependent_Soft_Pruning_final.pdf) | IEEE Trans. Image Process. |||",
"DCP-CAC": "|[Dynamical channel pruning by conditional accuracy change for deep neural networks](https://ieeexplore.ieee.org/abstract/document/9055425) |TNNLS|||",
"SMCP": "| [Soft Masking for Cost-Constrained Channel Pruning](https://link.springer.com/10.1007/978-3-031-20083-0_38) | ECCV | `F` | [PyTorch(Author)](https://github.com/NVlabs/SMCP) |",
"RNP": "| [Runtime Neural Pruning](https://papers.NeurIPS.cc/paper/6813-runtime-neural-pruning) | NeurIPS | `F` | - |",
"FBS": "| [Dynamic Channel Pruning: Feature Boosting and Suppression](https://arxiv.org/abs/1810.05331) | ICLR | `F` | [TensorFlow(Author)](https://github.com/deep-fry/mayo) |",
"ManiDP": "| [Manifold Regularized Dynamic Network Pruning](https://arxiv.org/abs/2103.05861) | CVPR | `F` | - |",
"DRLP": "| [Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning](https://proceedings.neurips.cc/paper/2020/hash/a914ecef9c12ffdb9bede64bb703d877-Abstract.html) | NeurIPS | `F` | - |",
"DDG": "| [Dynamic Dual Gating Neural Networks](https://openaccess.thecvf.com/content/ICCV2021/papers/Li_Dynamic_Dual_Gating_Neural_Networks_ICCV_2021_paper.pdf) | ICCV | `F` | [PyTorch(Author)](https://github.com/lfr-0531/DGNet) |",
"FTWT": "| [Fire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask PredictionFire Together Wire Together: A Dynamic Pruning Approach With Self-Supervised Mask Prediction](https://openaccess.thecvf.com/content/CVPR2022/html/Elkerdawy_Fire_Together_Wire_Together_A_Dynamic_Pruning_Approach_With_Self-Supervised_CVPR_2022_paper.html) | CVPR | `F` | - |",
"CDG": "| [Contrastive Dual Gating: Learning Sparse Features With Contrastive Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Meng_Contrastive_Dual_Gating_Learning_Sparse_Features_With_Contrastive_Learning_CVPR_2022_paper.html) | CVPR | `WF` | - |",
"AMC": "| [Amc: Automl for model compression and acceleration on mobile devices](https://arxiv.org/abs/1802.03494) | ECCV | `F` | [TensorFlow(3rd)](https://github.com/Tencent/PocketFlow#channel-pruning) |",
"AGMC": "| [Auto Graph Encoder-Decoder for Neural Network Pruning](https://openaccess.thecvf.com/content/ICCV2021/html/Yu_Auto_Graph_Encoder-Decoder_for_Neural_Network_Pruning_ICCV_2021_paper.html) | ICCV | `F` | - |",
"DECORE":"| [DECORE: Deep Compression With Reinforcement Learning](https://openaccess.thecvf.com/content/CVPR2022/html/Alwani_DECORE_Deep_Compression_With_Reinforcement_Learning_CVPR_2022_paper.html) | CVPR | `F` | - |",
"GNN-RL": "| [Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning](https://proceedings.mlr.press/v162/yu22e.html) | ICML | `F` | [PyTorch(Author)](https://github.com/yusx-swapp/GNN-RL-Model-Compression) |",
"AutoCompress": "| [AutoCompress: An Automatic DNN Structured Pruning Framework for Ultra-High Compression Rates](https://arxiv.org/abs/1907.03141) | AAAI | `F` | - |",
"RL-MCTS": "|[Channel pruning via lookahead search guided reinforcement learning](https://openaccess.thecvf.com/content/WACV2022/papers/Wang_Channel_Pruning_via_Lookahead_Search_Guided_Reinforcement_Learning_WACV_2022_paper.pdf) |WACV|||",
"DMCP": "| [DMCP: Differentiable Markov Channel Pruning for Neural Networks](https://arxiv.org/abs/2005.03354) | CVPR **(Oral)** | `F` | [TensorFlow(Author)](https://github.com/zx55/dmcp) |",
"DSA": "| [DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation](https://arxiv.org/abs/2004.02164) | ECCV | `F` | - |",
"DHP": "| [DHP: Differentiable Meta Pruning via HyperNetworks](https://arxiv.org/abs/2003.13683) | ECCV | `F` | [PyTorch(Author)](https://github.com/ofsoundof/dhp) |",
"PaS": "| [Pruning-as-Search: Efficient Neural Architecture Search via Channel Pruning and Structural Reparameterization](https://www.ijcai.org/proceedings/2022/449) | IJCAI | `F` | - |",
"LFPC": "| [Learning Filter Pruning Criteria for Deep Convolutional Neural Networks Acceleration](http://openaccess.thecvf.com/content_CVPR_2020/html/He_Learning_Filter_Pruning_Criteria_for_Deep_Convolutional_Neural_Networks_Acceleration_CVPR_2020_paper.html) | CVPR | `F` | - |",
"TAS": "| [Network Pruning via Transformable Architecture Search](https://arxiv.org/abs/1905.09717) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/D-X-Y/NAS-Projects) |",
"EE": "| [Exploration and Estimation for Model Compression](https://papers.nips.cc/paper/2021/hash/5227b6aaf294f5f027273aebf16015f2-Abstract.html) | ICCV | `F` | - |",
"DDNP": "| [Disentangled Differentiable Network Pruning](https://link.springer.com/10.1007/978-3-031-20083-0_20) | ECCV | `F` | - |",
"MFP": "|[Filter pruning by switching to neighboring cnns with good attributes](https://arxiv.org/pdf/1904.03961.pdf) | TNNLS |||",
"DNCP": "|[Model compression based on differentiable network channel pruning](https://ieeexplore.ieee.org/abstract/document/9758156) |TNNLS|||",
"DAIS": "|[Dais: Automatic channel pruning via differentiable annealing indicator search](https://arxiv.org/pdf/2011.02166.pdf) |TNNLS|||",
"ReCNAS": "|[ReCNAS: Resource-Constrained Neural Architecture Search Based on Differentiable Annealing and Dynamic Pruning](https://ieeexplore.ieee.org/document/9836970/) |TNNLS|||",
"MetaPruning": "| [MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning](https://arxiv.org/abs/1903.10258) | ICCV | `F` | [PyTorch(Author)](https://github.com/liuzechun/MetaPruning) |",
"ABCPruner": "| [Channel Pruning via Automatic Structure Search](https://arxiv.org/abs/2001.08565) | IJCAI | `F` | [PyTorch(Author)](https://github.com/lmbxmu/ABCPruner) |",
"CCEP": "| [Neural Network Pruning by Cooperative Coevolution](https://www.ijcai.org/proceedings/2022/667) | IJCAI | `F` | - |",
"EDropout": "|[EDropout: Energy-Based Dropout and Pruning of Deep Neural Networks](https://ieeexplore.ieee.org/document/9399169/) | TNNLS|||",
"RVNP": "| [Rethinking the Value of Network Pruning](https://arxiv.org/abs/1810.05270) | ICLR | `F` | [PyTorch(Author)](https://github.com/Eric-mingjie/rethinking-network-pruning) |",
"EB": "| [Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks](https://openreview.net/forum?id=BJxsrgStvr) | ICLR (**Spotlight**) | `WF` | [PyTorch(Author)](https://github.com/GATECH-EIC/Early-Bird-Tickets)|",
"ProsPr": "| [Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients](https://openreview.net/forum?id=AIgn9uwfcD1) | ICLR | `WF` | [PyTorch(Author)](https://github.com/mil-ad/prospr) |",
"EarlyCroP": "| [Winning the Lottery Ahead of Time: Efficient Early Network Pruning](https://proceedings.mlr.press/v162/rachwan22a.html) | ICML | `F` | [PyTorch(Author)](https://github.com/johnrachwan123/Early-Cropression-via-Gradient-Flow-Preservation) |",
"PaT": "| [When To Prune? A Policy Towards Early Structural Pruning](https://openaccess.thecvf.com/content/CVPR2022/html/Shen_When_To_Prune_A_Policy_Towards_Early_Structural_Pruning_CVPR_2022_paper.html) | CVPR | `F` | - |",
"PnS": "| [Plant 'n' Seek: Can You Find the Winning Ticket?](https://openreview.net/forum?id=9n9c8sf0xm) | ICLR | `F` | [PyTorch(Author)](http://www.github.com/RelationalML/PlantNSeek) |",
"SuperTickets": "| [SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning](https://link.springer.com/10.1007/978-3-031-20083-0_40) | ECCV | `F` | [PyTorch(Author)](https://github.com/GATECH-EIC/SuperTickets) |",
"Cunha22": "| [Proving the Lottery Ticket Hypothesis for Convolutional Neural Networks](https://openreview.net/forum?id=Vjki79-619-) | ICLR | `F` | [PyTorch(Author)](https://github.com/ArthurWalraven/cnnslth) |",
"RRCP": "| [Revisiting Random Channel Pruning for Neural Network Compression](https://openaccess.thecvf.com/content/CVPR2022/html/Li_Revisiting_Random_Channel_Pruning_for_Neural_Network_Compression_CVPR_2022_paper.html) | CVPR | `F` | [PyTorch(Author)](https://github.com/ofsoundof/random_channel_pruning)(Releasing) |",
"NPAS": "| [NPAS: A Compiler-aware Framework of Unified Network Pruning andArchitecture Search for Beyond Real-Time Mobile Acceleration](https://arxiv.org/abs/2012.00596) | CVPR | `F` | - |",
"DJPQ": "| [Differentiable Joint Pruning and Quantization for Hardware Efficiency](https://arxiv.org/abs/2007.10463) | ECCV | `Other` | - |",
"BB": "| [Bayesian Bits: Unifying Quantization and Pruning](https://arxiv.org/abs/2005.07093) | NeurIPS | `Other` | - |",
"IODF": "| [Fast Lossless Neural Compression with Integer-Only Discrete Flows](https://proceedings.mlr.press/v162/wang22a.html) | ICML | `F` | [PyTorch(Author)](https://github.com/thu-ml/IODF) |",
"APQ": "| [APQ: Joint Search for Network Architecture, Pruning and Quantization Policy](https://arxiv.org/abs/2006.08509) | CVPR | `F` | - |",
"Hinge": "| [Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression](https://arxiv.org/abs/2003.08935) | CVPR | `F` | [PyTorch(Author)](https://github.com/ofsoundof/group_sparsity) |",
"CC": "| [Towards Compact CNNs via Collaborative Compression](https://arxiv.org/abs/2105.11228) | CVPR | `F` | [PyTorch(Author)](https://github.com/liuguoyou/Towards-Compact-CNNs-via-Collaborative-Compression) |",
"NM": "| [Neuron Merging: Compensating for Pruned Neurons](https://arxiv.org/abs/2010.13160) | NeurIPS | `F` | [PyTorch(Author)](https://github.com/friendshipkim/neuron-merging) |",
"EDP": "|[EDP: An Efficient Decomposition and Pruning Scheme for Convolutional Neural Network Compression](https://ieeexplore.ieee.org/document/9246734/) |TNNLS|||",
"GBD": "| [Fast ConvNets Using Group-wise Brain Damage](https://openaccess.thecvf.com/content_cvpr_2016/papers/Lebedev_Fast_ConvNets_Using_CVPR_2016_paper.pdf) ||||",
"SWP": "| [Pruning Filter in Filter](https://arxiv.org/abs/2009.14410) | NeurIPS | `Other` | [PyTorch(Author)](https://github.com/fxmeng/Pruning-Filter-in-Filter) |",
"PCONV": "|[PCONV: The Missing but Desirable Sparsity in DNN Weight Pruning for Real-Time Execution on Mobile Devices](https://ojs.aaai.org/index.php/AAAI/article/view/5954) |AAAI||||",
"GKP-TMI": "| [Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions](https://openreview.net/forum?id=LdEhiMG9WLO) | ICLR | `F` | [PyTorch(Author)](https://github.com/choH/lottery_regulated_grouped_kernel_pruning) |",
"1xN": "|[1xN Pattern for Pruning Convolutional Neural Networks](https://ieeexplore.ieee.org/document/9847369/) |TPAMI||||",
"SDN": "|[Shallowing Deep Networks: Layer-Wise Pruning Based on Feature Representations](https://ieeexplore.ieee.org/document/8485719/) | TPAMI |||",
"JMDP": "|[Joint Multi-Dimension Pruning via Numerical Gradient Update](https://ieeexplore.ieee.org/document/9542852/) | IEEE Trans. Image Process. |||",
"SOKS": "|[SOKS: Automatic Searching of the Optimal Kernel Shapes for Stripe-Wise Network Pruning](https://ieeexplore.ieee.org/document/9755967/) |TNNLS|||",
"DPP": "|[Dynamic Probabilistic Pruning: A General Framework for Hardware-Constrained Pruning at Different Granularities](https://ieeexplore.ieee.org/document/9790881/) |TNNLS|||",
"JCW": "| [Multi-granularity Pruning for Model Acceleration on Mobile Devices](https://link.springer.com/10.1007/978-3-031-20083-0_29) | ECCV | `WF` | - |",
}
venue_mapping={
"CVPR" : "{Proc. IEEE Conf. Comput. Vis. Pattern Recog.}",
"ICCV" : "{Proc. Int. Conf. Comput. Vis.}",
"ICLR" : "{Proc. Int. Conf. Learn. Represent.}",
"ICML" : "{Proc. Int. Conf. Mach. Learn.}",
"ECCV" : "{Proc. Eur. Conf. Comput. Vis.}",
"NIPS" : "{Proc. Adv. Neural Inform. Process. Syst.}",
"AAAI" : "{Proc. AAAI Conf. Artif. Intell.}",
"IJCAI": "{Proc. Int. Joint Conf. Artif. Intell.}",
"WACV" : "{Proc. IEEE Winter Conf. Appl. Comput. Vis.}",
"ICASSP":"{Proc. IEEE Int. Conf. Acoust. Speech Signal Process.}",
"TPAMI": "{IEEE Trans. Pattern Anal. Mach. Intell.}",
"TNNLS": "{IEEE Trans. Neural Netw. Learn Syst.}",
"TC": "{IEEE Trans. Cybern.}",
"JMLR": "{J. Mach. Learn. Res.}",
"IJCV": "{Int. J. Comput. Vis.}",
}
citation_key_mapping = {'PFEC': 'liPruningFiltersEfficient2017', 'FPGM': 'heFilterPruningGeometric2019', 'RED': 'yvinecREDLookingRedundancies2021', 'RED++': 'yvinecREDDataFreePruning2022', 'COP': 'wangCOPCustomizedDeep2019', 'SRR': 'wangConvolutionalNeuralNetwork2021', 'CLR-RNF': 'linPruningNetworksCrossLayer2022', 'EPruner': 'linNetworkPruningUsing2022', 'CP': 'heChannelPruningAccelerating2017', 'HRank': 'linHRankFilterPruning2020', 'CBC': 'dubeyCoresetBasedNeuralNetwork2018', 'CHIP': 'suiCHIPCHannelIndependencebased2021', 'APoZ': 'huNetworkTrimmingDataDriven2016', 'DropNet': 'parikhProximalAlgorithms2014', 'LRMF': 'zhangFilterPruningLearned2021', 'GCNP': 'jiangChannelPruningUsing2022', 'ThiNet': 'luoThiNetFilterLevel2017', 'AOFP': 'dingApproximatedOracleFilter2019', 'GFS': 'yeGoodSubnetworksProvably2020', 'NISP': 'yuNISPPruningNetworks2018a', 'DCP': 'zhuangDiscriminationawareChannelPruning2018', 'PFP': 'liebenweinProvableFilterPruning2020', 'DLRFC': 'heFilterPruningFeature2022', 'NS': 'liuLearningEfficientConvolutional2017', 'GBN': 'youGateDecoratorGlobal2019', 'PR': 'zhuangNeuronlevelStructuredPruning2020', 'RSNLI': 'yeRethinkingSmallernormlessinformativeAssumption2018', 'SCP': 'kangOperationAwareSoftChannel2020', 'EagleEye': 'liEagleEyeFastSubnet2020', 'SSS': 'huangDataDrivenSparseStructure2018', 'GAL': 'linOptimalStructuredCNN2019', 'DMC': 'gaoDiscreteModelCompression2020', 'GDP-Guo': 'guoGDPStabilizedNeural2021', 'ResRep': 'dingResRepLosslessCNN2021', 'SCOP': 'tangSCOPScientificControl2020', 'BAR': 'lemaireStructuredPruningNeural2019', 'ABP': 'tianAddingPruningSparse2021', 'WhiteBox': 'zhangCarryingOutCNN2022', 'LeGR': 'chinEfficientModelCompression2020', 'ML1R': 'xieLearningOptimizedStructure2020', 'SSL': 'wenLearningStructuredSparsity2016', 'OICSR': 'liOICSROutInChannelSparsity2019', 'OTO': 'chenOnlyTrainOnce2021', 'GREG': 'wangNeuralPruningGrowing2022', 'Mol-16': 'molchanovPruningConvolutionalNeural2017', 'Mol-19': 'molchanovImportanceEstimationNeural2019', 'CCP': 'pengCollaborativeChannelPruning2019', 'ED': 'wangEigenDamageStructuredPruning2019', 'GFP': 'liuGroupFisherPruning2021', 'HAP': 'yuHessianAwarePruningOptimal2022', 'SOSP': 'nonnenmacherSOSPEfficientlyCapturing2022', 'VP': 'zhaoVariationalConvolutionalNeural2019', 'RBP': 'zhouAccelerateCNNRecursive2019', 'VIBNet': 'dai2018compressing', 'Horseshoe': 'louizosBayesianCompressionDeep2017', 'Log-normal': 'neklyudov2017structured', 'C-SGD': 'dingCentripetalSGDPruning2019', 'RMDA': 'huangTrainingStructuredNeural2022', 'Mehta-19': 'mehtaImplicitFilterLevel2019', 'EKG': 'leeEnsembleKnowledgeGuided2022', 'StructADMM': 'zhangStructADMMAchievingUltrahigh2022', 'Non-structured-ADMM': 'maNonStructuredDNNWeight2022', 'RollBack': 'fanBayesianOptimizationClustering2022', 'ST': 'lubanaGradientFlowFramework2020', 'SFP': 'heSoftFilterPruning2018', 'GDP-Lin': 'linAcceleratingConvolutionalNetworks2018', 'DPF': 'linDynamicModelPruning2022', 'CHEX': 'houCHEXCHannelEXploration2022', 'DSG': 'liuDynamicSparseGraph2019', 'SEP': 'dingWherePruneUsing2021', 'DCP-CAC': 'chenDynamicalChannelPruning2021', 'SMCP': 'humbleSoftMaskingCostConstrained2022', 'RNP': 'mengContrastiveDualGating2022', 'FBS': 'gaoDynamicChannelPruning2019', 'ManiDP': 'tangManifoldRegularizedDynamic2021', 'DRLP': 'chenStorageEfficientDynamic2020', 'DDG': 'liDynamicDualGating2021', 'FTWT': 'elkerdawyFireTogetherWire2022', 'CDG': 'mengContrastiveDualGating2022', 'AMC': 'heAMCAutoMLModel2018', 'AGMC': 'yuAutoGraphEncoderDecoder2021', 'DECORE': 'alwaniDECOREDeepCompression2022', 'GNN-RL': 'yuTopologyAwareNetworkPruning2022', 'AutoCompress': 'liuAutoCompressAutomaticDNN2020', 'RL-MCTS': 'wangChannelPruningLookahead2022', 'DMCP': 'guoDMCPDifferentiableMarkov2020', 'DSA': 'ningDSAMoreEfficient2020', 'DHP': 'liDHPDifferentiableMeta2020', 'PaS': 'liPruningasSearchEfficientNeural2022', 'LFPC': 'heLearningFilterPruning2020', 'TAS': 'dongNetworkPruningTransformable2019', 'EE': 'zhangExplorationEstimationModel2021', 'DDNP': 'gaoDisentangledDifferentiableNetwork2022', 'MFP': 'heFilterPruningSwitching2022', 'DNCP': 'zhengModelCompressionBased2022', 'DAIS': 'guanDAISAutomaticChannel2022', 'ReCNAS': 'pengReCNASResourceConstrainedNeural2022', 'MetaPruning': 'liuMetaPruningMetaLearning2019', 'ABCPruner': 'linChannelPruningAutomatic2020', 'CCEP': 'shangNeuralNetworkPruning2022', 'EDropout': 'salehinejadEDropoutEnergyBasedDropout2022', 'RVNP': 'liuRethinkingValueNetwork2019', 'EB': 'youDrawingEarlyBirdTickets2020', 'ProsPr': 'alizadehProspectPruningFinding2022', 'EarlyCroP': 'rachwanWinningLotteryAhead2022', 'PaT': 'shenWhenPrunePolicy2022', 'PnS': 'fischerPlantSeekCan2022', 'SuperTickets': 'youSuperTicketsDrawingTaskAgnostic2022', 'Cunha22': 'cunhaProvingLotteryTicket2022', 'RRCP': 'liRevisitingRandomChannel2022', 'NPAS': 'liNPASCompilerAwareFramework2021', 'DJPQ': 'wangDifferentiableJointPruning2020', 'BB': 'vanbaalenBayesianBitsUnifying2020', 'IODF': 'wangFastLosslessNeural2022', 'APQ': 'wangAPQJointSearch2020', 'Hinge': 'liGroupSparsityHinge2020', 'CC': 'liCompactCNNsCollaborative2021', 'NM': 'kimNeuronMergingCompensating2020', 'EDP': 'ruanEDPEfficientDecomposition2021', 'GBD': 'lebedevFastConvNetsUsing2016', 'SWP': 'mengPruningFilterFilter2020', 'PCONV': 'maPCONVMissingDesirable2020', 'GKP-TMI': 'zhongRevisitKernelPruning2022', '1xN': 'lin1xNPatternPruning2022', 'SDN': 'chenShallowingDeepNetworks2019', 'JMDP': 'liuJointMultiDimensionPruning2021', 'SOKS': 'liuSOKSAutomaticSearching2022', 'DPP': 'gonzalez-carabarinDynamicProbabilisticPruning2022', 'JCW': 'zhaoMultigranularityPruningModel2022'}
bibtex_mapping = {'PFEC': '@inproceedings{liPruningFiltersEfficient2017,\n title = {Pruning filters for efficient convnets},\n author = {Li, Hao and Kadav, Asim and Durdanovic, Igor and Samet, Hanan and Graf, Hans Peter},\n year = 2017,\n booktitle = ICLR\n}', 'FPGM': '@inproceedings{heFilterPruningGeometric2019,\n title = {Filter pruning via geometric median for deep convolutional neural networks acceleration},\n author = {He, Yang and Liu, Ping and Wang, Ziwei and Hu, Zhilan and Yang, Yi},\n year = 2019,\n booktitle = CVPR,\n pages = {4340--4349}', 'RED': '@inproceedings{yvinecREDLookingRedundancies2021,\n title = {RED: Looking for redundancies for data-freestructured compression of deep neural networks},\n author = {Yvinec, Edouard and Dapogny, Arnaud and Cord, Matthieu and Bailly, Kevin},\n year = 2021,\n booktitle = NIPS,\n pages = {20863--20873}', 'RED++': '@article{yvinecREDDataFreePruning2022,\n title = {RED++: Data-free pruning of deep neural networks via input splitting and output merging},\n author = {Yvinec, Edouard and Dapogny, Arnaud and Bailly, Kevin and Cord, Matthieu},\n year = 2022,\n journal = TPAMI,\n publisher = {IEEE}', 'COP': '@inproceedings{wangCOPCustomizedDeep2019,\n title = {COP: Customized deep model compression via regularized correlation-based filter-level pruning},\n author = {Wang, Wenxiao and Fu, Cong and Guo, Jishun and Cai, Deng and He, Xiaofei},\n year = 2019,\n booktitle = IJCAI,\n pages = {3785没3791}', 'SRR': '@inproceedings{wangConvolutionalNeuralNetwork2021,\n title = {Convolutional neural network pruning with structural redundancy reduction},\n author = {Wang, Zi and Li, Chengcheng and Wang, Xiangyang},\n year = 2021,\n booktitle = CVPR,\n pages = {14913--14922}', 'CLR-RNF': '@article{linPruningNetworksCrossLayer2022,\n title = {Pruning networks with cross-layer ranking \\& k-reciprocal nearest filters},\n author = {Lin, Mingbao and Cao, Liujuan and Zhang, Yuxin and Shao, Ling and Lin, Chia-Wen and Ji, Rongrong},\n year = 2022,\n journal = TNNLS,\n pages = {1--10}', 'EPruner': '@article{linNetworkPruningUsing2022,\n title = {Network pruning using adaptive exemplar filters},\n author = {Lin, Mingbao and Ji, Rongrong and Li, Shaojie and Wang, Yan and Wu, Yongjian and Huang, Feiyue and Ye, Qixiang},\n year = 2022,\n journal = TNNLS,\n volume = 33,\n number = 12,\n pages = {7357--7366}', 'CP': '@inproceedings{heChannelPruningAccelerating2017,\n title = {Channel pruning for accelerating very deep neural networks},\n author = {He, Yihui and Zhang, Xiangyu and Sun, Jian},\n year = 2017,\n booktitle = ICCV,\n pages = {1389--1397}', 'HRank': '@inproceedings{linHRankFilterPruning2020,\n title = {HRank: Filter pruning using high-rank feature map},\n author = {Lin, Mingbao and Ji, Rongrong and Wang, Yan and Zhang, Yichen and Zhang, Baochang and Tian, Yonghong and Shao, Ling},\n year = 2020,\n booktitle = CVPR,\n pages = {1529--1538}', 'CBC': '@inproceedings{dubeyCoresetBasedNeuralNetwork2018,\n title = {Coreset-based neural network compression},\n author = {Dubey, Abhimanyu and Chatterjee, Moitreya and Ahuja, Narendra},\n year = 2018,\n booktitle = ECCV,\n pages = {454--470}', 'CHIP': '@inproceedings{suiCHIPCHannelIndependencebased2021,\n title = {Chip: Channel independence-based pruning for compact neural networks},\n author = {Sui, Yang and Yin, Miao and Xie, Yi and Phan, Huy and Aliari Zonouz, Saman and Yuan, Bo},\n year = 2021,\n booktitle = NIPS,\n volume = 34,\n pages = {24604--24616}', 'APoZ': '@article{huNetworkTrimmingDataDriven2016,\n title = {Network trimming: A data-driven neuron pruning approach towards efficient deep architectures},\n author = {Hu, Hengyuan and Peng, Rui and Tai, Yu-Wing and Tang, Chi-Keung},\n year = 2016,\n journal = {arXiv preprint arXiv:1607.03250}', 'DropNet': '@article{parikhProximalAlgorithms2014,\n title = {Proximal algorithms},\n author = {Parikh, Neal and Boyd, Stephen},\n year = 2014,\n journal = {Found. Trends Optim.},\n volume = 1,\n number = 3,\n pages = {127--239}', 'LRMF': '@article{zhangFilterPruningLearned2021,\n title = {Filter pruning via learned representation median in the frequency domain},\n author = {Zhang, Xin and Xie, Weiying and Li, Yunsong and Lei, Jie and Du, Qian},\n year = 2021,\n journal = TC,\n pages = {1--11}', 'GCNP': '@inproceedings{jiangChannelPruningUsing2022,\n title = {On the channel pruning using graph convolution network for convolutional neural network acceleration},\n author = {Jiang, Di and Cao, Yuan and Yang, Qiang},\n year = 2022,\n month = 7,\n booktitle = IJCAI,\n pages = {3107--3113}', 'ThiNet': '@inproceedings{luoThiNetFilterLevel2017,\n title = {ThiNet: A filter level pruning method for deep neural network compression},\n author = {Luo, Jian-Hao and Wu, Jianxin and Lin, Weiyao},\n year = 2017,\n booktitle = CVPR,\n pages = {5058--5066}', 'AOFP': '@inproceedings{dingApproximatedOracleFilter2019,\n title = {Approximated oracle filter pruning for destructive cnn width optimization},\n author = {Ding, Xiaohan and Ding, Guiguang and Guo, Yuchen and Han, Jungong and Yan, Chenggang},\n year = 2019,\n booktitle = ICML,\n pages = {1607--1616}', 'GFS': '@inproceedings{yeGoodSubnetworksProvably2020,\n title = {Good subnetworks provably exist: Pruning via greedy forward selection},\n author = {Ye, Mao and Gong, Chengyue and Nie, Lizhen and Zhou, Denny and Klivans, Adam and Liu, Qiang},\n year = 2020,\n booktitle = ICML,\n pages = {10820--10830},\n organization = {PMLR}', 'NISP': '@inproceedings{yuNISPPruningNetworks2018a,\n title = {NISP: Pruning networks using neuron importance score propagation},\n author = {Yu, Ruichi and Li, Ang and Chen, Chun-Fu and Lai, Jui-Hsin and Morariu, Vlad I. and Han, Xintong and Gao, Mingfei and Lin, Ching-Yung and Davis, Larry S.},\n year = 2018,\n booktitle = CVPR,\n pages = {9194--9203}', 'DCP': '@inproceedings{zhuangDiscriminationawareChannelPruning2018,\n title = {Discrimination-aware channel pruning for deep neural networks},\n author = {Zhuang, Zhuangwei and Tan, Mingkui and Zhuang, Bohan and Liu, Jing and Guo, Yong and Wu, Qingyao and Huang, Junzhou and Zhu, Jinhui},\n year = 2018,\n booktitle = NIPS,\n pages = {883没894}', 'PFP': '@inproceedings{liebenweinProvableFilterPruning2020,\n title = {Provable filter pruning for efficient neural networks},\n author = {Liebenwein, Lucas and Baykal, Cenk and Lang, Harry and Feldman, Dan and Rus, Daniela},\n year = 2020,\n booktitle = ICLR\n}', 'DLRFC': '@inproceedings{heFilterPruningFeature2022,\n title = {Filter pruning via feature discrimination in deep neural networks},\n author = {He, Zhiqiang and Qian, Yaguan and Wang, Yuqi and Wang, Bin and Guan, Xiaohui and Gu, Zhaoquan and Ling, Xiang and Zeng, Shaoning and Wang, Haijiang and Zhou, Wujie},\n year = 2022,\n booktitle = ECCV,\n pages = {245--261},\n organization = {Springer}', 'NS': '@inproceedings{liuLearningEfficientConvolutional2017,\n title = {Learning efficient convolutional networks through network slimming},\n author = {Liu, Zhuang and Li, Jianguo and Shen, Zhiqiang and Huang, Gao and Yan, Shoumeng and Zhang, Changshui},\n year = 2017,\n booktitle = ICCV,\n pages = {2736--2744}', 'GBN': '@inproceedings{youGateDecoratorGlobal2019,\n title = {Gate Decorator: Global filter pruning method for accelerating deep convolutional neural networks},\n author = {You, Zhonghui and Yan, Kun and Ye, Jinmian and Ma, Meng and Wang, Ping},\n year = 2019,\n booktitle = NIPS\n}', 'PR': '@inproceedings{zhuangNeuronlevelStructuredPruning2020,\n title = {Neuron-level structured pruning using polarization regularizer},\n author = {Zhuang, Tao and Zhang, Zhixuan and Huang, Yuheng and Zeng, Xiaoyi and Shuang, Kai and Li, Xiang},\n year = 2020,\n booktitle = NIPS,\n volume = 33,\n pages = {9865--9877}', 'RSNLI': '@inproceedings{yeRethinkingSmallernormlessinformativeAssumption2018,\n title = {Rethinking the smaller-norm-less-informative assumption in channel pruning of convolution layers},\n author = {Ye, Jianbo and Lu, Xin and Lin, Zhe and Wang, James Z.},\n year = 2018,\n booktitle = ICLR\n}', 'SCP': '@inproceedings{kangOperationAwareSoftChannel2020,\n title = {Operation-aware soft channel pruning using differentiable masks},\n author = {Kang, Minsoo and Han, Bohyung},\n year = 2020,\n booktitle = ICML,\n pages = {5122--5131},\n organization = {PMLR}', 'EagleEye': '@inproceedings{liEagleEyeFastSubnet2020,\n title = {EagleEye: Fast sub-net evaluation for\xa0efficient neural network pruning},\n author = {Li, Bailin and Wu, Bowen and Su, Jiang and Wang, Guangrun},\n year = 2020,\n booktitle = ECCV,\n pages = {639--654},\n organization = {Springer}', 'SSS': '@inproceedings{huangDataDrivenSparseStructure2018,\n title = {Data-driven sparse structure selection for deep neural networks},\n author = {Huang, Zehao and Wang, Naiyan},\n year = 2018,\n booktitle = ECCV,\n pages = {304--320}', 'GAL': '@inproceedings{linOptimalStructuredCNN2019,\n title = {Towards optimal structured cnn pruning via generative adversarial learning},\n author = {Lin, Shaohui and Ji, Rongrong and Yan, Chenqian and Zhang, Baochang and Cao, Liujuan and Ye, Qixiang and Huang, Feiyue and Doermann, David},\n year = 2019,\n booktitle = CVPR,\n pages = {2790--2799}', 'DMC': '@inproceedings{gaoDiscreteModelCompression2020,\n title = {Discrete model compression with resource constraint for deep neural networks},\n author = {Gao, Shangqian and Huang, Feihu and Pei, Jian and Huang, Heng},\n year = 2020,\n booktitle = CVPR,\n pages = {1899--1908}', 'GDP-Guo': '@inproceedings{guoGDPStabilizedNeural2021,\n title = {GDP: Stabilized neural network pruning via gates with differentiable polarization},\n author = {Guo, Yi and Yuan, Huan and Tan, Jianchao and Wang, Zhangyang and Yang, Sen and Liu, Ji},\n year = 2021,\n booktitle = ICCV,\n pages = {5239--5250}', 'ResRep': '@inproceedings{dingResRepLosslessCNN2021,\n title = {ResRep: Lossless cnn pruning via decoupling remembering and forgetting},\n author = {Ding, Xiaohan and Hao, Tianxiang and Tan, Jianchao and Liu, Ji and Han, Jungong and Guo, Yuchen and Ding, Guiguang},\n year = 2021,\n booktitle = ICCV,\n pages = {4510--4520}', 'SCOP': '@inproceedings{tangSCOPScientificControl2020,\n title = {SCOP: Scientific control for reliable neural network pruning},\n author = {Tang, Yehui and Wang, Yunhe and Xu, Yixing and Tao, Dacheng and XU, Chunjing and Xu, Chao and Xu, Chang},\n year = 2020,\n booktitle = NIPS,\n pages = {10936--10947}', 'BAR': '@inproceedings{lemaireStructuredPruningNeural2019,\n title = {Structured pruning of neural networks with budget-aware regularization},\n author = {Lemaire, Carl and Achkar, Andrew and Jodoin, Pierre-Marc},\n year = 2019,\n booktitle = CVPR,\n pages = {9108--9116}', 'ABP': '@article{tianAddingPruningSparse2021,\n title = {Adding Before Pruning: Sparse filter fusion for deep convolutional neural networks via auxiliary attention},\n author = {Tian, Guanzhong and Sun, Yiran and Liu, Yuang and Zeng, Xianfang and Wang, Mengmeng and Liu, Yong and Zhang, Jiangning and Chen, Jun},\n year = 2021,\n journal = TNNLS,\n pages = {1--13}', 'WhiteBox': '@article{zhangCarryingOutCNN2022,\n title = {Carrying out cnn channel pruning in a white box},\n author = {Zhang, Yuxin and Lin, Mingbao and Lin, Chia-Wen and Chen, Jie and Wu, Yongjian and Tian, Yonghong and Ji, Rongrong},\n year = 2022,\n journal = TNNLS,\n pages = {1--10}', 'LeGR': '@inproceedings{chinEfficientModelCompression2020,\n title = {Towards efficient model compression via learned global ranking},\n author = {Chin, Ting-Wu and Ding, Ruizhou and Zhang, Cha and Marculescu, Diana},\n year = 2020,\n booktitle = CVPR,\n pages = {1518--1528}', 'ML1R': '@article{xieLearningOptimizedStructure2020,\n title = {Learning optimized structure of neural networks by hidden node pruning with \\$L\\_1\\$ regularization},\n author = {Xie, Xuetao and Zhang, Huaqing and Wang, Junze and Chang, Qin and Wang, Jian and Pal, Nikhil R.},\n year = 2020,\n journal = TC,\n volume = 50,\n number = 3,\n pages = {1333--1346}', 'SSL': '@inproceedings{wenLearningStructuredSparsity2016,\n title = {Learning structured sparsity in deep neural networks},\n author = {Wen, Wei and Wu, Chunpeng and Wang, Yandan and Chen, Yiran and Li, Hai},\n year = 2016,\n booktitle = NIPS,\n pages = {2082没2090}', 'OICSR': '@inproceedings{liOICSROutInChannelSparsity2019,\n title = {OICSR: Out-in-channel sparsity regularization for compact deep neural networks},\n author = {Li, Jiashi and Qi, Qi and Wang, Jingyu and Ge, Ce and Li, Yujian and Yue, Zhangzhang and Sun, Haifeng},\n year = 2019,\n booktitle = CVPR,\n pages = {7046--7055}', 'OTO': '@inproceedings{chenOnlyTrainOnce2021,\n title = {Only Train Once: A one-shot neural network training and pruning framework},\n author = {Chen, Tianyi and Ji, Bo and Ding, Tianyu and Fang, Biyi and Wang, Guanyi and Zhu, Zhihui and Liang, Luming and Shi, Yixin and Yi, Sheng and Tu, Xiao},\n year = 2021,\n booktitle = NIPS,\n volume = 34,\n pages = {19637--19651}', 'GREG': '@inproceedings{wangNeuralPruningGrowing2022,\n title = {Neural pruning via growing regularization},\n author = {Wang, Huan and Qin, Can and Zhang, Yulun and Fu, Yun},\n year = 2022,\n booktitle = ICLR\n}', 'Mol-16': '@inproceedings{molchanovPruningConvolutionalNeural2017,\n title = {Pruning convolutional neural networks for resource efficient inference},\n author = {Molchanov, Pavlo and Tyree, Stephen and Karras, Tero and Aila, Timo and Kautz, Jan},\n year = 2017,\n booktitle = ICLR\n}', 'Mol-19': '@inproceedings{molchanovImportanceEstimationNeural2019,\n title = {Importance estimation for neural network pruning},\n author = {Molchanov, Pavlo and Mallya, Arun and Tyree, Stephen and Frosio, Iuri and Kautz, Jan},\n year = 2019,\n booktitle = CVPR,\n pages = {11264--11272}', 'CCP': '@inproceedings{pengCollaborativeChannelPruning2019,\n title = {Collaborative channel pruning for deep networks},\n author = {Peng, Hanyu and Wu, Jiaxiang and Chen, Shifeng and Huang, Junzhou},\n year = 2019,\n booktitle = ICML,\n pages = {5113--5122},\n organization = {PMLR}', 'ED': '@inproceedings{wangEigenDamageStructuredPruning2019,\n title = {EigenDamage: Structured pruning in the kronecker-factored eigenbasis},\n author = {Wang, Chaoqi and Grosse, Roger and Fidler, Sanja and Zhang, Guodong},\n year = 2019,\n booktitle = ICML,\n pages = {6566--6575},\n organization = {PMLR}', 'GFP': '@inproceedings{liuGroupFisherPruning2021,\n title = {Group fisher pruning for practical network compression},\n author = {Liu, Liyang and Zhang, Shilong and Kuang, Zhanghui and Zhou, Aojun and Xue, Jing-Hao and Wang, Xinjiang and Chen, Yimin and Yang, Wenming and Liao, Qingmin and Zhang, Wayne},\n year = 2021,\n booktitle = ICML,\n pages = {7021--7032},\n organization = {PMLR}', 'HAP': '@inproceedings{yuHessianAwarePruningOptimal2022,\n title = {Hessian-aware pruning and optimal neural implant},\n author = {Yu, Shixing and Yao, Zhewei and Gholami, Amir and Dong, Zhen and Kim, Sehoon and Mahoney, Michael W and Keutzer, Kurt},\n year = 2022,\n booktitle = WACV,\n pages = {3880--3891}', 'SOSP': '@inproceedings{nonnenmacherSOSPEfficientlyCapturing2022,\n title = {SOSP: Efficiently capturing global correlations by second-order structured pruning},\n author = {Nonnenmacher, Manuel and Pfeil, Thomas and Steinwart, Ingo and Reeb, David},\n year = 2022,\n booktitle = ICLR\n}', 'VP': '@inproceedings{zhaoVariationalConvolutionalNeural2019,\n title = {Variational convolutional neural network pruning},\n author = {Zhao, Chenglong and Ni, Bingbing and Zhang, Jian and Zhao, Qiwei and Zhang, Wenjun and Tian, Qi},\n year = 2019,\n booktitle = CVPR,\n pages = {2780--2789}', 'RBP': '@inproceedings{zhouAccelerateCNNRecursive2019,\n title = {Accelerate cnn via recursive bayesian pruning},\n author = {Zhou, Yuefu and Zhang, Ya and Wang, Yanfeng and Tian, Qi},\n year = 2019,\n booktitle = ICCV,\n pages = {3306--3315}', 'VIBNet': '@inproceedings{dai2018compressing,\n title = {Compressing neural networks using the variational information bottleneck},\n author = {Dai, Bin and Zhu, Chen and Guo, Baining and Wipf, David},\n year = 2018,\n booktitle = ICML\n}', 'Horseshoe': '@inproceedings{louizosBayesianCompressionDeep2017,\n title = {Bayesian compression for deep learning},\n author = {Louizos, Christos and Ullrich, Karen and Welling, Max},\n year = 2017,\n booktitle = NIPS,\n pages = {3290没3300}', 'Log-normal': '@inproceedings{neklyudov2017structured,\n title = {Structured bayesian pruning via log-normal multiplicative noise},\n author = {Neklyudov, Kirill and Molchanov, Dmitry and Ashukha, Arsenii and Vetrov, Dmitry P},\n year = 2017,\n booktitle = NIPS,\n pages = {6778没6787}', 'C-SGD': '@inproceedings{dingCentripetalSGDPruning2019,\n title = {Centripetal sgd for pruning very deep convolutional networks with complicated structure},\n author = {Ding, Xiaohan and Ding, Guiguang and Guo, Yuchen and Han, Jungong},\n year = 2019,\n booktitle = CVPR\n}', 'RMDA': '@inproceedings{huangTrainingStructuredNeural2022,\n title = {Training structured neural networks through manifold identification and variance reduction},\n author = {Huang, Zih-Syuan and Lee, Ching-pei},\n year = 2022,\n booktitle = ICLR\n}', 'Mehta-19': '@inproceedings{mehtaImplicitFilterLevel2019,\n title = {On implicit filter level sparsity in convolutional neural networks},\n author = {Mehta, Dushyant and Kim, Kwang In and Theobalt, Christian},\n year = 2019,\n booktitle = CVPR,\n pages = {520--528}', 'EKG': '@inproceedings{leeEnsembleKnowledgeGuided2022,\n title = {Ensemble knowledge guided sub-network search and fine-tuning for filter pruning},\n author = {Lee, Seunghyun and Song, Byung Cheol},\n year = 2022,\n booktitle = ECCV,\n pages = {569--585},\n organization = {Springer}', 'StructADMM': '@article{zhangStructADMMAchievingUltrahigh2022,\n title = {StructADMM: Achieving ultrahigh efficiency in structured pruning for dnns},\n author = {Zhang, Tianyun and Ye, Shaokai and Feng, Xiaoyu and Ma, Xiaolong and Zhang, Kaiqi and Li, Zhengang and Tang, Jian and Liu, Sijia and Lin, Xue and Liu, Yongpan and Fardad, Makan and Wang, Yanzhi},\n year = 2022,\n journal = TNNLS,\n volume = 33,\n number = 5,\n pages = {2259--2273}', 'Non-structured-ADMM': '@article{maNonStructuredDNNWeight2022,\n title = {Non-structured dnn weight pruning霉is it beneficial in any platform?},\n author = {Ma, Xiaolong and Lin, Sheng and Ye, Shaokai and He, Zhezhi and Zhang, Linfeng and Yuan, Geng and Tan, Sia Huat and Li, Zhengang and Fan, Deliang and Qian, Xuehai and Lin, Xue and Ma, Kaisheng and Wang, Yanzhi},\n year = 2022,\n journal = TNNLS,\n volume = 33,\n number = 9,\n pages = {4930--4944}', 'RollBack': '@inproceedings{fanBayesianOptimizationClustering2022,\n title = {Bayesian optimization with clustering and rollback for cnn auto pruning},\n author = {Fan, Hanwei and Mu, Jiandong and Zhang, Wei},\n year = 2022,\n booktitle = ECCV,\n pages = {494--511},\n organization = {Springer}', 'ST': '@inproceedings{lubanaGradientFlowFramework2020,\n title = {A gradient flow framework for analyzing network pruning},\n author = {Lubana, Ekdeep Singh and Dick, Robert},\n year = 2022,\n booktitle = ICLR\n}', 'SFP': '@inproceedings{heSoftFilterPruning2018,\n title = {Soft filter pruning for accelerating deep convolutional neural networks},\n author = {He, Yang and Kang, Guoliang and Dong, Xuanyi and Fu, Yanwei and Yang, Yi},\n year = 2018,\n booktitle = IJCAI,\n pages = {2234没2240}', 'GDP-Lin': '@inproceedings{linAcceleratingConvolutionalNetworks2018,\n title = {Accelerating convolutional networks via global \\& dynamic filter pruning},\n author = {Lin, Shaohui and Ji, Rongrong and Li, Yuchao and Wu, Yongjian and Huang, Feiyue and Zhang, Baochang},\n year = 2018,\n booktitle = IJCAI,\n volume = 2,\n number = 7,\n pages = 8,\n organization = {Stockholm}', 'DPF': '@inproceedings{linDynamicModelPruning2022,\n title = {Dynamic model pruning with feedback},\n author = {Lin, Tao and Stich, Sebastian U. and Barba, Luis and Dmitriev, Daniil and Jaggi, Martin},\n year = 2022,\n booktitle = ICLR\n}', 'CHEX': '@inproceedings{houCHEXCHannelEXploration2022,\n title = {Chex: Channel exploration for cnn model compression},\n author = {Hou, Zejiang and Qin, Minghai and Sun, Fei and Ma, Xiaolong and Yuan, Kun and Xu, Yi and Chen, Yen-Kuang and Jin, Rong and Xie, Yuan and Kung, Sun-Yuan},\n year = 2022,\n booktitle = CVPR,\n pages = {12287--12298}', 'DSG': '@inproceedings{liuDynamicSparseGraph2019,\n title = {Dynamic sparse graph for efficient deep learning},\n author = {Liu, Liu and Deng, Lei and Hu, Xing and Zhu, Maohua and Li, Guoqi and Ding, Yufei and Xie, Yuan},\n year = 2019,\n booktitle = ICLR\n}', 'SEP': '@article{dingWherePruneUsing2021,\n title = {Where to Prune: Using lstm to guide data-dependent soft pruning},\n author = {Ding, Guiguang and Zhang, Shuo and Jia, Zizhou and Zhong, Jing and Han, Jungong},\n year = 2021,\n journal = {IEEE Trans. Image Process.},\n volume = 30,\n pages = {293--304}', 'DCP-CAC': '@article{chenDynamicalChannelPruning2021,\n title = {Dynamical channel pruning by conditional accuracy change for deep neural networks},\n author = {Chen, Zhiqiang and Xu, Ting-Bing and Du, Changde and Liu, Cheng-Lin and He, Huiguang},\n year = 2021,\n journal = TNNLS,\n volume = 32,\n number = 2,\n pages = {799--813}', 'SMCP': '@inproceedings{humbleSoftMaskingCostConstrained2022,\n title = {Soft masking for cost-constrained channel pruning},\n author = {Humble, Ryan and Shen, Maying and Latorre, Jorge Albericio and Darve, Eric and Alvarez, Jose},\n year = 2022,\n booktitle = ECCV,\n pages = {641--657},\n organization = {Springer}', 'RNP': '@inproceedings{mengContrastiveDualGating2022,\n title = {Contrastive Dual Gating: Learning sparse features with contrastive learning},\n author = {Meng, Jian and Yang, Li and Shin, Jinwoo and Fan, Deliang and Seo, Jae-sun},\n year = 2022,\n booktitle = CVPR,\n pages = {12257--12265}', 'FBS': '@inproceedings{gaoDynamicChannelPruning2019,\n title = {Dynamic channel pruning: Feature boosting and suppression},\n author = {Gao, Xitong and Zhao, Yiren and Dudziak, {\\L}ukasz and Mullins, Robert and Xu, Cheng-zhong},\n year = 2019,\n booktitle = ICLR\n}', 'ManiDP': '@inproceedings{tangManifoldRegularizedDynamic2021,\n title = {Manifold regularized dynamic network pruning},\n author = {Tang, Yehui and Wang, Yunhe and Xu, Yixing and Deng, Yiping and Xu, Chao and Tao, Dacheng and Xu, Chang},\n year = 2021,\n booktitle = CVPR,\n pages = {5018--5028}', 'DRLP': '@inproceedings{chenStorageEfficientDynamic2020,\n title = {Storage efficient and dynamic flexible runtime channel pruning via deep reinforcement learning},\n author = {Chen, Jianda and Chen, Shangyu and Pan, Sinno Jialin},\n year = 2020,\n booktitle = NIPS,\n volume = 33,\n pages = {14747--14758}', 'DDG': '@inproceedings{liDynamicDualGating2021,\n title = {Dynamic dual gating neural networks},\n author = {Li, Fanrong and Li, Gang and He, Xiangyu and Cheng, Jian},\n year = 2021,\n booktitle = ICCV,\n pages = {5330--5339}', 'FTWT': '@inproceedings{elkerdawyFireTogetherWire2022,\n title = {Fire Together Wire Together: A dynamic pruning approach with self-supervised mask prediction},\n author = {Elkerdawy, Sara and Elhoushi, Mostafa and Zhang, Hong and Ray, Nilanjan},\n year = 2022,\n booktitle = CVPR,\n pages = {12454--12463}', 'CDG': '@inproceedings{mengContrastiveDualGating2022,\n title = {Contrastive Dual Gating: Learning sparse features with contrastive learning},\n author = {Meng, Jian and Yang, Li and Shin, Jinwoo and Fan, Deliang and Seo, Jae-sun},\n year = 2022,\n booktitle = CVPR,\n pages = {12257--12265}', 'AMC': '@inproceedings{heAMCAutoMLModel2018,\n title = {AMC: Automl for model compression and acceleration on mobile devices},\n author = {He, Yihui and Lin, Ji and Liu, Zhijian and Wang, Hanrui and Li, Li-Jia and Han, Song},\n year = 2018,\n booktitle = ECCV,\n pages = {784--800}', 'AGMC': '@inproceedings{yuAutoGraphEncoderDecoder2021,\n title = {Auto graph encoder-decoder for neural network pruning},\n author = {Yu, Sixing and Mazaheri, Arya and Jannesari, Ali},\n year = 2021,\n booktitle = ICCV,\n pages = {6362--6372}', 'DECORE': '@inproceedings{alwaniDECOREDeepCompression2022,\n title = {DECORE: Deep compression with reinforcement learning},\n author = {Alwani, Manoj and Wang, Yang and Madhavan, Vashisht},\n year = 2022,\n booktitle = CVPR,\n pages = {12349--12359}', 'GNN-RL': '@inproceedings{yuTopologyAwareNetworkPruning2022,\n title = {Topology-aware network pruning using multi-stage graph embedding and reinforcement learning},\n author = {Yu, Sixing and Mazaheri, Arya and Jannesari, Ali},\n year = 2022,\n booktitle = ICML,\n pages = {25656--25667},\n organization = {PMLR}', 'AutoCompress': '@inproceedings{liuAutoCompressAutomaticDNN2020,\n title = {AutoCompress: An automatic dnn structured pruning framework for ultra-high compression rates},\n author = {Liu, Ning and Ma, Xiaolong and Xu, Zhiyuan and Wang, Yanzhi and Tang, Jian and Ye, Jieping},\n year = 2020,\n booktitle = AAAI,\n volume = 34,\n number = {04},\n pages = {4876--4883}', 'RL-MCTS': '@inproceedings{wangChannelPruningLookahead2022,\n title = {Channel pruning via lookahead search guided reinforcement learning},\n author = {Wang, Zi and Li, Chengcheng},\n year = 2022,\n booktitle = WACV,\n pages = {2029--2040}', 'DMCP': '@inproceedings{guoDMCPDifferentiableMarkov2020,\n title = {DMCP: Differentiable markov channel pruning for neural networks},\n author = {Guo, Shaopeng and Wang, Yujie and Li, Quanquan and Yan, Junjie},\n year = 2020,\n booktitle = CVPR,\n pages = {1539--1547}', 'DSA': '@inproceedings{ningDSAMoreEfficient2020,\n title = {DSA: More efficient budgeted pruning via differentiable sparsity allocation},\n author = {Ning, Xuefei and Zhao, Tianchen and Li, Wenshuo and Lei, Peng and Wang, Yu and Yang, Huazhong},\n year = 2020,\n booktitle = ECCV,\n pages = {592--607},\n organization = {Springer}', 'DHP': '@inproceedings{liDHPDifferentiableMeta2020,\n title = {DHP: Differentiable meta pruning via hypernetworks},\n author = {Li, Yawei and Gu, Shuhang and Zhang, Kai and Van Gool, Luc and Timofte, Radu},\n year = 2020,\n booktitle = ECCV,\n pages = {608--624},\n organization = {Springer}', 'PaS': '@inproceedings{liPruningasSearchEfficientNeural2022,\n title = {Pruning-as-Search: Efficient neural architecture search via channel pruning and structural reparameterization},\n author = {Li, Yanyu and Zhao, Pu and Yuan, Geng and Lin, Xue and Wang, Yanzhi and Chen, Xin},\n year = 2022,\n month = 7,\n booktitle = IJCAI,\n pages = {3236--3242}', 'LFPC': '@inproceedings{heLearningFilterPruning2020,\n title = {Learning filter pruning criteria for deep convolutional neural networks acceleration},\n author = {He, Yang and Ding, Yuhang and Liu, Ping and Zhu, Linchao and Zhang, Hanwang and Yang, Yi},\n year = 2020,\n booktitle = CVPR,\n pages = {2009--2018}', 'TAS': '@inproceedings{dongNetworkPruningTransformable2019,\n title = {Network pruning via transformable architecture search},\n author = {Dong, Xuanyi and Yang, Yi},\n year = 2019,\n booktitle = NIPS,\n pages = {760--771}', 'EE': '@inproceedings{zhangExplorationEstimationModel2021,\n title = {Exploration and estimation for model compression},\n author = {Zhang, Yanfu and Gao, Shangqian and Huang, Heng},\n year = 2021,\n booktitle = ICCV,\n pages = {487--496}', 'DDNP': '@inproceedings{gaoDisentangledDifferentiableNetwork2022,\n title = {Disentangled differentiable network pruning},\n author = {Gao, Shangqian and Huang, Feihu and Zhang, Yanfu and Huang, Heng},\n year = 2022,\n booktitle = ECCV,\n pages = {328--345},\n organization = {Springer}', 'MFP': '@article{heFilterPruningSwitching2022,\n title = {Filter pruning by switching to neighboring cnns with good attributes},\n author = {He, Yang and Liu, Ping and Zhu, Linchao and Yang, Yi},\n year = 2022,\n journal = TNNLS,\n pages = {1--13}', 'DNCP': '@article{zhengModelCompressionBased2022,\n title = {Model compression based on differentiable network channel pruning},\n author = {Zheng, Yu-Jie and Chen, Si-Bao and Ding, Chris H. Q. and Luo, Bin},\n year = 2022,\n journal = TNNLS,\n pages = {1--10}', 'DAIS': '@article{guanDAISAutomaticChannel2022,\n title = {DAIS: Automatic channel pruning via differentiable annealing indicator search},\n author = {Guan, Yushuo and Liu, Ning and Zhao, Pengyu and Che, Zhengping and Bian, Kaigui and Wang, Yanzhi and Tang, Jian},\n year = 2022,\n journal = TNNLS,\n pages = {1--12}', 'ReCNAS': '@article{pengReCNASResourceConstrainedNeural2022,\n title = {ReCNAS: Resource-constrained neural architecture search based on differentiable annealing and dynamic pruning},\n author = {Peng, Cheng and Li, Yangyang and Shang, Ronghua and Jiao, Licheng},\n year = 2022,\n journal = TNNLS,\n pages = {1--15}', 'MetaPruning': '@inproceedings{liuMetaPruningMetaLearning2019,\n title = {MetaPruning: Meta learning for automatic neural network channel pruning},\n author = {Liu, Zechun and Mu, Haoyuan and Zhang, Xiangyu and Guo, Zichao and Yang, Xin and Cheng, Kwang-Ting and Sun, Jian},\n year = 2019,\n booktitle = ICCV,\n pages = {3296--3305}', 'ABCPruner': '@inproceedings{linChannelPruningAutomatic2020,\n title = {Channel pruning via automatic structure search},\n author = {Lin, Mingbao and Ji, Rongrong and Zhang, Yuxin and Zhang, Baochang and Wu, Yongjian and Tian, Yonghong},\n year = 2020,\n booktitle = IJCAI,\n pages = {673--679}', 'CCEP': '@inproceedings{shangNeuralNetworkPruning2022,\n title = {Neural network pruning by cooperative coevolution},\n author = {Shang, Haopu and Wu, Jia-Liang and Hong, Wenjing and Qian, Chao},\n year = 2022,\n month = 7,\n booktitle = IJCAI,\n pages = {4814--4820}', 'EDropout': '@article{salehinejadEDropoutEnergyBasedDropout2022,\n title = {EDropout: Energy-based dropout and pruning of deep neural networks},\n author = {Salehinejad, Hojjat and Valaee, Shahrokh},\n year = 2022,\n journal = TNNLS,\n volume = 33,\n number = 10,\n pages = {5279--5292}', 'RVNP': '@inproceedings{liuRethinkingValueNetwork2019,\n title = {Rethinking the value of network pruning},\n author = {Liu, Zhuang and Sun, Mingjie and Zhou, Tinghui and Huang, Gao and Darrell, Trevor},\n year = 2019,\n booktitle = ICLR\n}', 'EB': '@inproceedings{youDrawingEarlyBirdTickets2020,\n title = {Drawing early-bird tickets: Towards more efficient training of deep networks},\n author = {You, Haoran and Li, Chaojian and Xu, Pengfei and Fu, Yonggan and Wang, Yue and Chen, Xiaohan and Baraniuk, Richard G and Wang, Zhangyang and Lin, Yingyan},\n year = 2020,\n booktitle = ICLR\n}', 'ProsPr': '@inproceedings{alizadehProspectPruningFinding2022,\n title = {Prospect pruning: Finding trainable weights at initialization using meta-gradients},\n author = {Alizadeh, Milad and Tailor, Shyam A and Zintgraf, Luisa M and van Amersfoort, Joost and Farquhar, Sebastian and Lane, Nicholas Donald and Gal, Yarin},\n year = 2022,\n booktitle = ICLR\n}', 'EarlyCroP': '@inproceedings{rachwanWinningLotteryAhead2022,\n title = {Winning the Lottery Ahead of Time: Efficient early network pruning},\n author = {Rachwan, John and Z鈦縢ner, Daniel and Charpentier, Bertrand and Geisler, Simon and Ayle, Morgane and G鈦縩nemann, Stephan},\n year = 2022,\n booktitle = ICLR\n}', 'PaT': '@inproceedings{shenWhenPrunePolicy2022,\n title = {When to prune? a policy towards early structural pruning},\n author = {Shen, Maying and Molchanov, Pavlo and Yin, Hongxu and Alvarez, Jose M},\n year = 2022,\n booktitle = CVPR,\n pages = {12247--12256}', 'PnS': "@inproceedings{fischerPlantSeekCan2022,\n title = {Plant'n'Seek: Can you find the winning ticket?},\n author = {Fischer, Jonas and Burkholz, Rebekka},\n year = 2022,\n booktitle = ICLR\n}", 'SuperTickets': '@inproceedings{youSuperTicketsDrawingTaskAgnostic2022,\n title = {SuperTickets: Drawing task-agnostic lottery tickets from supernets via jointly architecture searching and parameter pruning},\n author = {You, Haoran and Li, Baopu and Sun, Zhanyi and Ouyang, Xu and Lin, Yingyan},\n year = 2022,\n booktitle = ECCV,\n pages = {674--690},\n organization = {Springer}', 'Cunha22': '@inproceedings{cunhaProvingLotteryTicket2022,\n title = {Proving the lottery ticket hypothesis for convolutional neural networks},\n author = {Cunha, Arthur da and Natale, Emanuele and Viennot, Laurent},\n year = 2022,\n booktitle = ICLR\n}', 'RRCP': '@inproceedings{liRevisitingRandomChannel2022,\n title = {Revisiting random channel pruning for neural network compression},\n author = {Li, Yawei and Adamczewski, Kamil and Li, Wen and Gu, Shuhang and Timofte, Radu and Van Gool, Luc},\n year = 2022,\n booktitle = CVPR,\n pages = {191--201}', 'NPAS': '@inproceedings{liNPASCompilerAwareFramework2021,\n title = {NPAS: A compiler-aware framework of unified network pruning and architecture search for beyond real-time mobile acceleration},\n author = {Li, Zhengang and Yuan, Geng and Niu, Wei and Zhao, Pu and Li, Yanyu and Cai, Yuxuan and Shen, Xuan and Zhan, Zheng and Kong, Zhenglun and Jin, Qing and Chen, Zhiyu and Liu, Sijia and Yang, Kaiyuan and Ren, Bin and Wang, Yanzhi and Lin, Xue},\n year = 2021,\n booktitle = CVPR,\n pages = {14255--14266}', 'DJPQ': '@inproceedings{wangDifferentiableJointPruning2020,\n title = {Differentiable joint pruning and quantization for hardware efficiency},\n author = {Wang, Ying and Lu, Yadong and Blankevoort, Tijmen},\n year = 2020,\n booktitle = ECCV,\n pages = {259--277}', 'BB': '@inproceedings{vanbaalenBayesianBitsUnifying2020,\n title = {Bayesian bits: Unifying quantization and pruning},\n author = {van Baalen, Mart and Louizos, Christos and Nagel, Markus and Amjad, Rana Ali and Wang, Ying and Blankevoort, Tijmen and Welling, Max},\n year = 2020,\n booktitle = NIPS,\n pages = {5741没-5752}', 'IODF': '@inproceedings{wangFastLosslessNeural2022,\n title = {Fast lossless neural compression with integer-only discrete flows},\n author = {Wang, Siyu and Chen, Jianfei and Li, Chongxuan and Zhu, Jun and Zhang, Bo},\n year = 2022,\n booktitle = ICML,\n pages = {22562--22575},\n organization = {PMLR}', 'APQ': 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= 2022,\n journal = TPAMI,\n publisher = {IEEE}', 'SDN': '@article{chenShallowingDeepNetworks2019,\n title = {Shallowing deep networks: Layer-wise pruning based on feature representations},\n author = {Chen, Shi and Zhao, Qi},\n year = 2018,\n journal = TPAMI,\n publisher = {IEEE},\n volume = 41,\n number = 12,\n pages = {3048--3056}', 'JMDP': '@article{liuJointMultiDimensionPruning2021,\n title = {Joint multi-dimension pruning via numerical gradient update},\n author = {Liu, Zechun and Zhang, Xiangyu and Shen, Zhiqiang and Wei, Yichen and Cheng, Kwang-Ting and Sun, Jian},\n year = 2021,\n journal = {IEEE Trans. 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