# Awesome-Pruning **Repository Path**: dengxuezheng/Awesome-Pruning ## Basic Information - **Project Name**: Awesome-Pruning - **Description**: A curated list of neural network pruning resources. - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-09-13 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # Awesome Pruning [![Awesome](https://awesome.re/badge.svg)](https://awesome.re) A curated list of neural network pruning and related resources. Inspired by [awesome-deep-vision](https://github.com/kjw0612/awesome-deep-vision), [awesome-adversarial-machine-learning](https://github.com/yenchenlin/awesome-adversarial-machine-learning), [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers) and [Awesome-NAS](https://github.com/D-X-Y/Awesome-NAS). Please feel free to [pull requests](https://github.com/he-y/awesome-Pruning/pulls) or [open an issue](https://github.com/he-y/awesome-Pruning/issues) to add papers. ## Table of Contents - [Type of Pruning](#type-of-pruning) - [2019 Venues](#2019) - [2018 Venues](#2018) - [2017 Venues](#2017) - [2016 Venues](#2016) - [2015 Venues](#2015) ### Type of Pruning | Type | `F` | `W` | `Other` | |:------------|:--------------:|:----------------------:|:----------:| | Explanation | Filter pruning | Weight pruning | other types | ### 2019 | Title | Venue | Type | Code | |:--------|:--------:|:--------:|:--------:| | [Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration](https://arxiv.org/abs/1811.00250) | CVPR **(Oral)** | `F` |[github](https://github.com/he-y/filter-pruning-geometric-median)| | [Towards Optimal Structured CNN Pruning via Generative Adversarial Learning](https://arxiv.org/abs/1903.09291) | CVPR | `F` | [github](https://github.com/ShaohuiLin/GAL) | | [Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure](https://arxiv.org/abs/1904.03837) | CVPR | `F` | [github](https://github.com/ShawnDing1994/Centripetal-SGD)| | [On Implicit Filter Level Sparsity in Convolutional Neural Networks](https://arxiv.org/abs/1811.12495) | CVPR | `F` | - | | [Structured Pruning of Neural Networks with Budget-Aware Regularization](https://arxiv.org/abs/1811.09332) | CVPR | `F` | -| | [Importance Estimation for Neural Network Pruning](http://jankautz.com/publications/Importance4NNPruning_CVPR19.pdf) | CVPR | `F` | [github](https://github.com/NVlabs/Taylor_pruning)| | [Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search](https://arxiv.org/abs/1903.03777) | CVPR | `Other` | [github](https://github.com/lixincn2015/Partial-Order-Pruning) | | [Variational Convolutional Neural Network Pruning](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhao_Variational_Convolutional_Neural_Network_Pruning_CVPR_2019_paper.pdf) | CVPR | - | -| | [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://arxiv.org/abs/1803.03635) | ICLR **(Best)** | `W` | [github](https://github.com/google-research/lottery-ticket-hypothesis)| | [Rethinking the Value of Network Pruning](https://arxiv.org/abs/1810.05270) | ICLR | `F` | [github](https://github.com/Eric-mingjie/rethinking-network-pruning)| | [Dynamic Channel Pruning: Feature Boosting and Suppression](https://arxiv.org/abs/1810.05331)| ICLR | `F` | [github](https://github.com/deep-fry/mayo)| | [SNIP: Single-shot Network Pruning based on Connection Sensitivity](https://arxiv.org/abs/1810.02340)| ICLR | `F` | [github](https://github.com/namhoonlee/snip-public)| | [Dynamic Sparse Graph for Efficient Deep Learning](https://arxiv.org/abs/1810.00859) | ICLR | `F` | [github](https://github.com/mtcrawshaw/dynamic-sparse-graph)| | [Collaborative Channel Pruning for Deep Networks](http://proceedings.mlr.press/v97/peng19c.html)| ICML | `F` | -| | [Approximated Oracle Filter Pruning for Destructive CNN Width Optimization github](https://arxiv.org/abs/1905.04748)| ICML | `F` | -| | [EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis4](https://arxiv.org/abs/1905.05934)| ICML | `W` | [github](https://github.com/alecwangcq/EigenDamage-Pytorch)| ### 2018 | Title | Venue | Type | Code | |:--------|:--------:|:--------:|:--------:| | [Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers](https://arxiv.org/abs/1802.00124)| ICLR | `F` | [github](https://github.com/jack-willturner/batchnorm-pruning)| | [To prune, or not to prune: exploring the efficacy of pruning for model compression](https://arxiv.org/abs/1710.01878)| ICLR | `W` | -| | [Discrimination-aware Channel Pruning for Deep Neural Networks](https://arxiv.org/abs/1810.11809)| NIPS | `F` | [github](https://github.com/SCUT-AILab/DCP)| | [Frequency-Domain Dynamic Pruning for Convolutional Neural Networks](https://papers.nips.cc/paper/7382-frequency-domain-dynamic-pruning-for-convolutional-neural-networks.pdf)| NIPS | `W` | - | | [Amc: Automl for model compression and acceleration on mobile devices](https://arxiv.org/abs/1802.03494)| ECCV | `F` | [github](https://github.com/Tencent/PocketFlow#channel-pruning)| | [Data-Driven Sparse Structure Selection for Deep Neural Networks](https://arxiv.org/abs/1707.01213)| ECCV | `F` | [github](https://github.com/TuSimple/sparse-structure-selection)| | [Coreset-Based Neural Network Compression](https://arxiv.org/abs/1807.09810) | ECCV | `F` | [github](https://github.com/metro-smiles/CNN_Compression)| |[Constraint-Aware Deep Neural Network Compression](http://www.sfu.ca/~ftung/papers/constraintaware_eccv18.pdf) | ECCV | `W` | [github](https://github.com/ChanganVR/ConstraintAwareCompression)| |[A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers](https://arxiv.org/abs/1804.03294)| ECCV | `W` | [github](https://github.com/KaiqiZhang/admm-pruning)| | [PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning](https://arxiv.org/abs/1711.05769)| CVPR | `F` | [github](https://github.com/arunmallya/packnet)| | [NISP: Pruning Networks using Neuron Importance Score Propagation](https://arxiv.org/abs/1711.05908)| CVPR | `F` | -| | [CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization](http://www.sfu.ca/~ftung/papers/clipq_cvpr18.pdf)| CVPR | `W` | -| | [“Learning-Compression” Algorithms for Neural Net Pruning](http://faculty.ucmerced.edu/mcarreira-perpinan/papers/cvpr18.pdf)| CVPR | `W` | -| | [Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks](https://arxiv.org/abs/1808.06866)| IJCAI | `F` | [github](https://github.com/he-y/soft-filter-pruning)| ### 2017 | Title | Venue | Type | Code | |:--------|:--------:|:--------:|:--------:| | [Pruning Filters for Efficient ConvNets](https://arxiv.org/abs/1608.08710)| ICLR | `F` | [github](https://github.com/Eric-mingjie/rethinking-network-pruning/tree/master/imagenet/l1-norm-pruning)| |[Pruning Convolutional Neural Networks for Resource Efficient Inference](https://arxiv.org/abs/1611.06440)| ICLR | `W` | [github](https://github.com/Tencent/PocketFlow#channel-pruning)| |[Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee](https://arxiv.org/abs/1611.05162)| NIPS | `W` | [github](https://github.com/DNNToolBox/Net-Trim-v1)| |[Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon](https://arxiv.org/abs/1705.07565)| NIPS | `W` | [github](https://github.com/csyhhu/L-OBS)| |[Runtime Neural Pruning](https://papers.nips.cc/paper/6813-runtime-neural-pruning) | NIPS | `F` | - | | [Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning](https://arxiv.org/abs/1611.05128)|CVPR|`F` |-| | [ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression](https://arxiv.org/abs/1707.06342)|ICCV|`F` | [github](https://github.com/Roll920/ThiNet)| | [Channel pruning for accelerating very deep neural networks](https://arxiv.org/abs/1707.06168)|ICCV|`F` | [github](https://github.com/yihui-he/channel-pruning)| | [Learning Efficient Convolutional Networks Through Network Slimming](https://arxiv.org/abs/1708.06519)|ICCV|`F` | [github](https://github.com/Eric-mingjie/network-slimming)| ### 2016 | Title | Venue | Type | Code | |:--------|:--------:|:--------:|:--------:| | [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://arxiv.org/abs/1510.00149) | ICLR **(Best)** | `W` | [github](https://github.com/songhan/Deep-Compression-AlexNet)| | [Dynamic Network Surgery for Efficient DNNs](https://arxiv.org/abs/1608.04493) | NIPS | `W` | [github](https://github.com/yiwenguo/Dynamic-Network-Surgery)| ### 2015 | Title | Venue | Type | Code | |:--------|:--------:|:--------:|:--------:| | [Learning both Weights and Connections for Efficient Neural Networks](https://arxiv.org/abs/1506.02626) | NIPS | `W` |-| ## Related Repo [Awesome-model-compression-and-acceleration](https://github.com/memoiry/Awesome-model-compression-and-acceleration) [EfficientDNNs](https://github.com/MingSun-Tse/EfficientDNNs) [Embedded-Neural-Network](https://github.com/ZhishengWang/Embedded-Neural-Network) [awesome-AutoML-and-Lightweight-Models](https://github.com/guan-yuan/awesome-AutoML-and-Lightweight-Models) [Model-Compression-Papers](https://github.com/chester256/Model-Compression-Papers) [knowledge-distillation-papers](https://github.com/lhyfst/knowledge-distillation-papers) [Network-Speed-and-Compression](https://github.com/mrgloom/Network-Speed-and-Compression)