AlexNet http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
ZFNet Visualizing and Understanding Convolutional Networks https://arxiv.org/abs/1311.2901
GoogLeNet, Inceptionv1(Going deeper with convolutions) https://arxiv.org/abs/1409.4842
Batch Normalization https://arxiv.org/abs/1502.03167
Inceptionv3(Rethinking the Inception Architecture for Computer Vision) https://arxiv.org/abs/1512.00567
Inceptionv4, Inception-ResNet https://arxiv.org/abs/1602.07261
Xception(Deep Learning with Depthwise Separable Convolutions) https://arxiv.org/abs/1610.02357
ResNeXt https://arxiv.org/abs/1611.05431
DenseNet https://arxiv.org/abs/1608.06993
NASNet-A(Learning Transferable Architectures for Scalable Image Recognition) https://arxiv.org/abs/1707.07012
SENet(Squeeze-and-Excitation Networks) https://arxiv.org/abs/1709.01507
MobileNet(v1) https://arxiv.org/abs/1704.04861
MobileNet(v2) https://arxiv.org/abs/1801.04381
MobileNet(v3) https://arxiv.org/abs/1905.02244
ShuffleNet(v1) https://arxiv.org/abs/1707.01083
ShuffleNet(v2) https://arxiv.org/abs/1807.11164
Bag of Tricks for Image Classification with Convolutional Neural Networks https://arxiv.org/abs/1812.01187
EfficientNet(v1) https://arxiv.org/abs/1905.11946
EfficientNet(v2) https://arxiv.org/abs/2104.00298
NFNets(High-Performance Large-Scale Image Recognition Without Normalization) https://arxiv.org/abs/2102.06171
Vision Transformer https://arxiv.org/abs/2010.11929
DeiT(Training data-efficient image transformers ) https://arxiv.org/abs/2012.12877
Swin Transformer https://arxiv.org/abs/2103.14030
Swin Transformer V2: Scaling Up Capacity and Resolution https://arxiv.org/abs/2111.09883
BEiT: BERT Pre-Training of Image Transformers https://arxiv.org/abs/2106.08254
MAE(Masked Autoencoders Are Scalable Vision Learners) https://arxiv.org/abs/2111.06377
ConvNeXt(A ConvNet for the 2020s) https://arxiv.org/abs/2201.03545