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
Y. Lecun, L. Bottou, Y. Bengio, P. Haffner

2012

AlexNet

ImageNet Classification with Deep ConvolutionalNeural Networks
Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton

2013

ZFNet

Visualizing and Understanding Convolutional Networks
Matthew D Zeiler, Rob Fergus

2014

VGG

Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan, Andrew Zisserman

GoogLeNet(Inception)

Going Deeper with Convolutions
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
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jonathon Shlens, Zbigniew Wojna

ResNet

Deep Residual Learning for Image Recognition
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
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi

SqueezeNet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer

FractalNet

FractalNet: Ultra-Deep Neural Networks without Residuals
Gustav Larsson, Michael Maire, Gregory Shakhnarovich

WideResNet

Wide Residual Networks
Sergey Zagoruyko, Nikos Komodakis

DenseNet

Densely Connected Convolutional Networks
Gao Huang, Zhuang Liu, Laurens van der Maaten, Kilian Q. Weinberger

Xception

Xception: Deep Learning with Depthwise Separable Convolutions
François Chollet

PyramidNet

Deep Pyramidal Residual Networks
Dongyoon Han, Jiwhan Kim, Junmo Kim

ResNeXt

Aggregated Residual Transformations for Deep Neural Networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, Kaiming He

2017

MobileNet V1

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, Hartwig Adam

2018

MobileNet V2

MobileNetV2: Inverted Residuals and Linear Bottlenecks
Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen

ShuffleNet V2

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ningning Ma, Xiangyu Zhang, Hai-Tao Zheng, Jian Sun

MnasNet

MnasNet: Platform-Aware Neural Architecture Search for Mobile
Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, Mark Sandler, Andrew Howard, Quoc V. Le

2019

MobileNet V3

Searching for MobileNetV3
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
Mingxing Tan, Quoc V. Le

GhostNet

GhostNet: More Features from Cheap Operations
Kai Han, Yunhe Wang, Qi Tian, Jianyuan Guo, Chunjing Xu, Chang Xu

2020

RegNet

Designing Network Design Spaces
Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár

ResNeSt

ResNeSt: Split-Attention Networks
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
Xiaohan Ding, Xiangyu Zhang, Ningning Ma, Jungong Han, Guiguang Ding, Jian Sun

NFNet

High-Performance Large-Scale Image Recognition Without Normalization
Andrew Brock, Soham De, Samuel L. Smith, Karen Simonyan

EfficientNetV2

EfficientNetV2: Smaller Models and Faster Training
Mingxing Tan, Quoc V. Le

2022

ConvNeXt

A ConvNet for the 2020s
Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feichtenhofer, Trevor Darrell, Saining Xie

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