Deep Compression/Acceleration（模型压缩加速总结）

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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

1. 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