Image Classification Paper List(1998~2021)

• Image Classificaiton Task와 관련하여 1998년부터 2021년까지 제안된 다양한 딥러닝 기반 논문들에 대해 Years 별로 목록를 만들어봄. 
• Network Name은 저자가 특별히 칭한 경우에는 약어로, 그렇지 않은 경우에는 Full Name으로 표기함. 또한, 논문에서 따로 명시를 하지 않은 경우에는 실험에 사용된 Network Name으로 표기함.  
• 논문들은 https://archive.org/ 를 기준으로 정리했으며, 제출년도가 동일한 논문들은 제출날짜 별로 따로 정렬하지 않았음. 또한, 논문이 여러 버전을 가지고 있는 경우에는 최초에 제출된 버전을 기준으로 제출년도를 기입함. 
• archive로 검색이 안되는 논문들의 경우 검색 가능한 해당 논문의 Link를 기입함.   

Years	Network Name	Title	Link
1998	LeNet-5	Gradient-based learning applied to document recognition	http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
2012	AlexNet	ImageNet Classification with Deep Convolutional Neural Networks	https://papers.nips.cc/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdf
2013	ZFNet	Visualizing and Understanding Convolutional Networks	https://arxiv.org/abs/1311.2901v3
2013	NIN	Network In Network	https://arxiv.org/abs/1312.4400
2014	VGGNet	Very Deep Convolutional Networks for Large-Scale Image Recognition	https://arxiv.org/abs/1409.1556
2014	GoogLeNet (Inception v1)	Going deeper with convolutions	https://arxiv.org/abs/1409.4842
2015	GoogLeNet (Inception v2~v3)	Rethinking the inception architecture for computer vision	https://arxiv.org/abs/1512.00567
2015	ResNet	Deep residual learning for image recognition	https://arxiv.org/abs/1512.03385
2015	pre-activation  ResNet	Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification	https://arxiv.org/abs/1502.01852
2016	GoogLeNet (Inception v4, Inception-ResNet)	Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning	https://arxiv.org/abs/1602.07261
2016	WRN	Wide Residual Networks	https://arxiv.org/abs/1605.07146
2016	SDR	Deep Networks with Stochastic Depth	https://arxiv.org/abs/1603.09382
2016	RiR	Resnet in Resnet: Generalizing Residual Architectures	https://arxiv.org/abs/1603.08029
2016	SqueezeNet	SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size	https://arxiv.org/abs/1602.07360
2016	DenseNet	Densely Connected Convolutional Networks	https://arxiv.org/abs/1608.06993
2016	Xception	Xception: Deep Learning with Depthwise Separable Convolutions	https://arxiv.org/abs/1610.02357
2016	ResNeXt	Aggregated Residual Transformations for Deep Neural Networks	https://arxiv.org/abs/1611.05431
2016	PolyNet	PolyNet: A Pursuit of Structural Diversity in Very Deep Networks	https://arxiv.org/abs/1611.05725
2016	PyramidNet	Deep Pyramidal Residual Networks	https://arxiv.org/abs/1610.02915v4
2016	RoR	Residual Networks of Residual Networks: Multilevel Residual Networks	https://arxiv.org/abs/1608.02908
2016	FractalNet	FractalNet: Ultra-Deep Neural Networks without Residuals	https://arxiv.org/abs/1605.07648
2016	DMRNet	Deep Convolutional Neural Networks with Merge-and-Run Mappings	https://arxiv.org/abs/1611.07718
2017	ShuffleNet(v1)	ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices	https://arxiv.org/abs/1707.01083
2017	IGCNets(IGCV1)	Interleaved Group Convolutions for Deep Neural Networks	https://arxiv.org/abs/1707.02725
2017	MSDNet	Multi-Scale Dense Networks for Resource Efficient Image Classification	https://arxiv.org/abs/1703.09844
2017	PNASNet	Progressive Neural Architecture Search	https://arxiv.org/abs/1712.00559v3
2017	Residual Attention Network	Residual Attention Network for Image Classification	https://arxiv.org/abs/1704.06904
2017	DPN	Dual Path Networks	https://arxiv.org/abs/1707.01629
2017	SENet	Squeeze-and-Excitation Networks	https://arxiv.org/abs/1709.01507
2017	CondenseNet	CondenseNet: An Efficient DenseNet using Learned Group Convolutions	https://arxiv.org/abs/1711.09224
2017	NASNet	Learning Transferable Architectures for Scalable  Image Recognition	https://arxiv.org/abs/1707.07012
2017	MobileNet v1	MobileNets: Efficient Convolutional Neural  Networks for Mobile Vision Applications	https://arxiv.org/abs/1704.04861
2018	ShuffleNet(v2)	ShuffleNet V2: Practical Guidelines for Efficient  CNN Architecture Design	https://arxiv.org/abs/1807.11164
2018	AmoebaNet	Regularized Evolution for Image Classifier Architecture Search	https://arxiv.org/abs/1802.01548v7
2018	MnasNet	MnasNet: Platform-Aware Neural Architecture  Search for Mobile	https://arxiv.org/abs/1807.11626
2018	IGCNets(IGCV2)	IGCV2: Interleaved Structured Sparse Convolutional Neural Networks	https://arxiv.org/abs/1804.06202
2018	IGCNets(IGCV3)	IGCV3: Interleaved Low-Rank Group Convolutions for Efficient Deep Neural Networks	https://arxiv.org/abs/1806.00178
2018	MobileNet v2	MobileNetV2: Inverted Residuals and Linear Bottlenecks	https://arxiv.org/abs/1801.04381
2018	Adversarial Inception v3, Ensemble Adversarial Inception ResNet v2	Adversarial Attacks and Defences Competition	https://arxiv.org/abs/1804.00097
2018	Deep Layer  Aggregation	Deep Layer Aggregation	https://arxiv.org/abs/1707.06484
2018	FBNet	FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search	https://arxiv.org/abs/1812.03443
2018	Instagram  ResNeXt WSL	Exploring the Limits of Weakly Supervised Pretraining	https://arxiv.org/abs/1805.00932
2018	ResNet-D	Bag of Tricks for Image Classification with Convolutional Neural Networks	https://arxiv.org/abs/1812.01187
2019	SSL ResNet, SSL ResNeXT, SWSL ResNet, SWSL ResNeXt	Billion-scale semi-supervised learning for image classification	https://arxiv.org/abs/1905.00546
2019	SPNASNet	Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours	https://arxiv.org/abs/1904.02877
2019	SKNets	Selective Kernel Networks	https://arxiv.org/abs/1903.06586
2019	Res2Net, Res2NeXt	Res2Net: A New Multi-scale Backbone  Architecture	https://arxiv.org/abs/1904.01169
2019	Noisy Student	Self-training with Noisy Student improves ImageNet classification	https://arxiv.org/abs/1911.04252
2019	MixNet	MixConv: Mixed Depthwise Convolutional Kernels	https://arxiv.org/abs/1907.09595
2019	MobileNet v3	Searching for MobileNetV3	https://arxiv.org/abs/1905.02244
2019	FishNet	FishNet: A Versatile Backbone for Image, Region, and Pixel Level Prediction	https://arxiv.org/abs/1901.03495
2019	GhostNet	GhostNet: More Features from Cheap Operations	https://arxiv.org/abs/1911.11907
2019	CSPNet	CSPNet: A New Backbone that can Enhance Learning Capability of CNN	https://arxiv.org/abs/1911.11929
2019	EfficientNet	EfficientNet: Rethinking Model Scaling for  Convolutional Neural Networks	https://arxiv.org/abs/1905.11946
2019	BiT	Big Transfer (BiT): General Visual Representation Learning	https://arxiv.org/abs/1912.11370
2019	ECA-Net	ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks	https://arxiv.org/abs/1910.03151
2019	VoVNet	An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection	https://arxiv.org/abs/1904.09730
2019	HRNet	High-Resolution Representations for Labeling  Pixels and Regions	https://arxiv.org/abs/1904.04514
2020	RegNet	Designing Network Design Spaces	https://arxiv.org/abs/2003.13678
2020	ResNeSt	ResNeSt: Split-Attention Networks	https://arxiv.org/abs/2004.08955
2020	ReXNet	Rethinking Channel Dimensions for Efficient Model Design	https://arxiv.org/abs/2007.00992
2020	TResNet	TResNet: High Performance GPU-Dedicated Architecture	https://arxiv.org/abs/2003.13630
2020	iGPT	Generative Pretraining from Pixels	https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf
2020	ViT	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	https://arxiv.org/abs/2010.11929
2020	DeiT	Training data-efficient image transformers & distillation through attention	https://arxiv.org/abs/2012.12877
2021	MLP-Mixer	MLP-Mixer: An all-MLP Architecture for Vision	https://arxiv.org/abs/2105.01601
2021	Swin Transformer	Swin Transformer: Hierarchical Vision Transformer using Shifted Windows	https://arxiv.org/abs/2103.14030
2021	CSWin Transformer	CSWin Transformer: A General Vision Transformer Backbone with Cross-Shaped Windows	https://arxiv.org/abs/2107.00652
2021	ViT_P, ViT_C	Early Convolutions Help Transformers See Better	https://arxiv.org/abs/2106.14881
2021	CoAtNets	CoAtNet: Marrying Convolution and Attention for All Data Sizes	https://arxiv.org/abs/2106.04803
2021	ViTs-SAM	When Vision Transformers Outperform ResNets without Pretraining or Strong Data Augmentations	https://arxiv.org/abs/2106.01548
2021	gMLP	Pay Attention to MLPs	https://arxiv.org/abs/2105.08050
2021	RVT	Towards Robust Vision Transformer	https://arxiv.org/abs/2105.07926
2021	DnC	Divide and Contrast: Self-supervised Learning from Uncurated Data	https://arxiv.org/abs/2105.08054
2021	PVT	Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions	https://arxiv.org/abs/2102.12122
2021	PiT	Rethinking Spatial Dimensions of Vision Transformers	https://arxiv.org/abs/2103.16302