CNNモデル論文リンク集

主要なCNNモデルの論文へのリンクを、発表順に論文執筆者名とともに記載する。

##1998
###LeNet
[Gradient-based learning applied to document recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-98.pdf)
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner

##2012
### AlexNet
[ImageNet Classification with Deep ConvolutionalNeural Networks](http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf)
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

##2013
###ZFNet
[Visualizing and Understanding Convolutional Networks](https://arxiv.org/abs/1311.2901)
Matthew D Zeiler, Rob Fergus

## 2014
### VGG
[Very Deep Convolutional Networks for Large-Scale Image Recognition](https://arxiv.org/abs/1409.1556)
Karen Simonyan, Andrew Zisserman


###GoogLeNet(Inception)
[Going Deeper with Convolutions](https://arxiv.org/abs/1409.4842)
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich


##2015
### Inception V2/V3
[Rethinking the Inception Architecture for Computer Vision](https://arxiv.org/abs/1512.00567)
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna

###ResNet
[Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)
Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun


##2016

### Inception V4 / InceptionResNet V2
[Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261)
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi


### SqueezeNet
[SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size](https://arxiv.org/abs/1602.07360)
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer


###FractalNet
[FractalNet: Ultra-Deep Neural Networks without Residuals](https://arxiv.org/abs/1605.07648)
Gustav Larsson, Michael Maire, Gregory Shakhnarovich

###WideResNet
[Wide Residual Networks](https://arxiv.org/abs/1605.07146)
Sergey Zagoruyko, Nikos Komodakis

### DenseNet

[Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993)
Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger


###Xception
[Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357)
François Chollet


### PyramidNet
[Deep Pyramidal Residual Networks](https://arxiv.org/abs/1610.02915)
Dongyoon Han, Jiwhan Kim, Junmo Kim


###ResNeXt
[Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/abs/1611.05431)
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He


## 2017

###MobileNet V1
[MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://arxiv.org/abs/1704.04861)
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam


###ShuffleNet
[ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://arxiv.org/abs/1707.01083)
Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin, Jian Sun


### NASNet
[Learning Transferable Architectures for Scalable Image Recognition](https://arxiv.org/abs/1707.07012)
Barret Zoph, Vijay Vasudevan, Jonathon Shlens, Quoc V. Le



### SENet
[Squeeze-and-Excitation Networks](https://arxiv.org/abs/1709.01507)
Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Enhua Wu


##2018

###MobileNet V2
[MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/abs/1801.04381)
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen


###ShuffleNet V2
[ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://arxiv.org/abs/1807.11164)
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun


###MnasNet
[MnasNet: Platform-Aware Neural Architecture Search for Mobile](https://arxiv.org/abs/1807.11626)
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le


## 2019
### MobileNet V3
[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244)
Andrew Howard, Mark Sandler, Grace Chu, Liang-Chieh Chen, Bo Chen, Mingxing Tan, Weijun Wang, Yukun Zhu, Ruoming Pang, Vijay Vasudevan, Quoc V. Le, Hartwig Adam

### EfficientNet
[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
Mingxing Tan, Quoc V. Le

###GhostNet
[GhostNet: More Features from Cheap Operations](https://arxiv.org/abs/1911.11907)
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu

##2020
###RegNet
[Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár

###ResNeSt
[ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Haibin Lin, Zhi Zhang, Yue Sun, Tong He, Jonas Mueller, R. Manmatha, Mu Li, Alexander Smola

##2021
###RepVGG
[RepVGG: Making VGG-style ConvNets Great Again](https://arxiv.org/abs/2101.03697)
Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun

###NFNet
[High-Performance Large-Scale Image Recognition Without Normalization](https://arxiv.org/abs/2102.06171)
Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan

###EfficientNetV2
[EfficientNetV2: Smaller Models and Faster Training](https://arxiv.org/abs/2104.00298)
Mingxing Tan, Quoc V. Le

##2022
###ConvNeXt
[A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie

#関連記事

https://qiita.com/T-STAR/items/d7a9e08caec5cd4c3f74

https://qiita.com/T-STAR/items/2d9df002da5f75d08f81