Deep Compression/Acceleration：模型压缩加速论文汇总

栏目: 数据库 · 发布时间: 5年前

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作者 | Mars_WH

来源 | 

blog.csdn.net/hw5226349/article/details/84888416

原文 | http://bbs.cvmart.net/topics/352

深度学习（Deep Learning）因其计算复杂度或参数冗余，在一些场景和设备上限制了相应的模型部署，需要借助模型压缩、优化加速、异构计算等方法突破瓶颈。其中模型压缩算法能够有效降低参数冗余，从而减少存储占用、通信带宽和计算复杂度，有助于深度学习的应用部署，本文汇总了近几年模型压缩方面的相关研究paper，欢迎收藏阅读~

结构structure

Searching for MobileNetV3

arxiv:https://arxiv.org/abs/1905.02244v1

中文解读： 重磅！MobileNetV3 来了！

[BMVC2018] IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
https://arxiv.org/abs/1806.00178

github: https://github.com/homles11/IGCV3

[CVPR2018] IGCV2: Interleaved Structured Sparse Convolutional Neural Networks

arxiv: https://arxiv.org/abs/1804.06202

[CVPR2018] MobileNetV2: Inverted Residuals and Linear Bottlenecks

arxiv: https://arxiv.org/abs/1801.04381

github: https://github.com/tensorflow/models/tree/master/research/slim/nets/mobilenet

[ECCV2018] ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

arxiv: https://arxiv.org/abs/1807.11164

量化quantization

Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

intro:二值网络

 https://arxiv.org/abs/1602.02830

github:  https://github.com/MatthieuCourbariaux/BinaryNet

https://github.com/itayhubara/BinaryNet

[ACM2017] FINN: A Framework for Fast, Scalable Binarized Neural Network Inference

intro:二值网络

 http://www.idi.ntnu.no/~yamanu/2017-fpga-finn-preprint.pdf

github: https://github.com/Xilinx/FINN

[CVPR2016] DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

intro:低bit位

 https://arxiv.org/abs/1606.06160

github: https://github.com/tensorpack/tensorpack/tree/master/examples/DoReFa-Net

[CVPR2016] XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

intro:darknet团队出品

 https://arxiv.org/abs/1603.05279

github: https://github.com/allenai/XNOR-Net

[CVPR2016] Ternary Weight Networks

arxiv: https://arxiv.org/abs/1605.04711

github: https://github.com/fengfu-chris/caffe-twns

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

Google出品

 https://arxiv.org/abs/1712.05877

github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/quantize

[ACM2017] Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations

intro:QNNs

 https://arxiv.org/abs/1609.07061

github: https://github.com/peisuke/qnn

Two-Step Quantization for Low-bit Neural Networks

paper: http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Two-Step_Quantization_for_CVPR_2018_paper.pdf

剪枝pruning

通道裁剪channel pruning

[NIPS2018] Discrimination-aware Channel Pruning for Deep Neural Networks

arxiv: https://arxiv.org/abs/1810.11809

github: https://github.com/Tencent/PocketFlow支持DisChnPrunedLearner

[ICCV2017] Channel Pruning for Accelerating Very Deep Neural Networks

intro:Lasso回归

 https://arxiv.org/abs/1707.06168

github: https://github.com/yihui-he/channel-pruning

[ECCV2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

intro:自动学习优化

 https://arxiv.org/abs/1802.03494

https://www.jiqizhixin.com/articles/AutoML-for-Model-Compression-and-Acceleration-on-Mobile-Devices论文翻译

github: https://github.com/Tencent/PocketFlow

[ICCV2017] Learning Efficient Convolutional Networks through Network Slimming

intro:Zhuang Liu

 https://arxiv.org/abs/1708.06519

github: https://github.com/Eric-mingjie/network-slimming

https://github.com/foolwood/pytorch-slimming

[ICLR2018] Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers

arxiv: https://arxiv.org/abs/1802.00124

github:[PyTorch] https://github.com/jack-willturner/batchnorm-pruning

[TensorFlow] https://github.com/bobye/batchnorm_prune

[CVPR2017] NISP: Pruning Networks using Neuron Importance Score Propagation

arxiv: https://arxiv.org/abs/1711.05908

[ICCV2017] ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression

web: http://lamda.nju.edu.cn/luojh/project/ThiNet_ICCV17/ThiNet_ICCV17_CN.html

github: https://github.com/Roll920/ThiNet

https://github.com/Roll920/ThiNet_Code

稀疏sparsity

SBNet: Sparse Blocks Network for Fast Inference

intro: Uber

 https://arxiv.org/abs/1801.02108

github: https://github.com/uber/sbnet

To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression

intro:稀疏

 https://arxiv.org/abs/1710.01878

github: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning

Submanifold Sparse Convolutional Networks

intro:Facebook

 https://arxiv.org/abs/1706.01307

github: https://github.com/facebookresearch/SparseConvNet

融合fusion

蒸馏distillation

[NIPS2014] Distilling the Knowledge in a Neural Network

intro:Hinton出品

 https://arxiv.org/abs/1503.02531

github: https://github.com/peterliht/knowledge-distillation-pytorch

综合comprehensive

[ICLR2016] Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding

intro:开创先河

 https://arxiv.org/abs/1510.00149

github: https://github.com/songhan

Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification

intro:实验比较多，适合工程化

arxiv:

https://arxiv.org/abs/1709.02929

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Deep Compression/Acceleration：模型压缩加速论文汇总

觉得有用麻烦给个在看啦~    Deep Compression/Acceleration：模型压缩加速论文汇总


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