原文链接：https://blog.csdn.net/hw5226349/article/details/84888416
本文系转载，感谢原文博主的分享!
结构`structure`
[BMVC2018] IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
[CVPR2018] IGCV2: Interleaved Structured Sparse Convolutional Neural Networks
[CVPR2018] MobileNetV2: Inverted Residuals and Linear Bottlenecks
[ECCV2018] ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
量化`quantization`
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
[ACM2017] FINN: A Framework for Fast, Scalable Binarized Neural Network Inference
[CVPR2016] DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
[CVPR2016] XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
[CVPR2016] Ternary Weight Networks Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
[ACM2017] Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
Two-Step Quantization for Low-bit Neural Networks
剪枝`pruning`
通道裁剪`channel pruning`
[NIPS2018] Discrimination-aware Channel Pruning for Deep Neural Networks
[ICCV2017] Channel Pruning for Accelerating Very Deep Neural Networks
[ECCV2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices
[ICCV2017] Learning Efficient Convolutional Networks through Network Slimming
[ICLR2018] Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
[CVPR2017] NISP: Pruning Networks using Neuron Importance Score Propagation
[ICCV2017] ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
稀疏`sparsity`
SBNet: Sparse Blocks Network for Fast Inference
To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression
Submanifold Sparse Convolutional Networks
融合`fusion`
蒸馏`distillation`
[NIPS2014] Distilling the Knowledge in a Neural Network
综合`comprehensive`
[ICLR2016] Deep Compression: Compressing Deep Neural Network with Pruning, Trained Quantization and Huffman Coding Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification
根据个人理解将模型压缩方面研究分为以下七个方向：
结构structure
[BMVC2018] IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks
intro:
arxiv:https://arxiv.org/abs/1806.00178
github:https://github.com/homles11/IGCV3
[CVPR2018] IGCV2: Interleaved Structured Sparse Convolutional Neural Networks
intro:
arxiv:https://arxiv.org/abs/1804.06202
同上
[CVPR2018] MobileNetV2: Inverted Residuals and Linear Bottlenecks
intro:
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
intro:
arxiv:https://arxiv.org/abs/1807.11164
github:
量化quantization
Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
intro:二值网络
arxiv: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
pdf: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
arxiv: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
arxiv:https://arxiv.org/abs/1603.05279
github:https://github.com/allenai/XNOR-Net
[CVPR2016] Ternary Weight Networks
intro:
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
arxiv: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
arxiv:https://arxiv.org/abs/1609.07061
github:https://github.com/peisuke/qnn
Two-Step Quantization for Low-bit Neural Networks
intro:
paper:http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Two-Step_Quantization_for_CVPR_2018_paper.pdf
github:
剪枝pruning
通道裁剪channel pruning
[NIPS2018] Discrimination-aware Channel Pruning for Deep Neural Networks
intro:
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
arxiv: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
arxiv: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
arxiv: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
intro:
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
intro:
arxiv:https://arxiv.org/abs/1711.05908
[ICCV2017] ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
intro:
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
arxiv: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
arxiv:https://arxiv.org/abs/1710.01878
github:https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/model_pruning
Submanifold Sparse Convolutional Networks
intro:Facebook
arxiv: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
arxiv: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
arxiv: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