图像分类(CLAS)#
Num |
Title |
Field |
Desc |
Author |
Time |
read |
---|---|---|---|---|---|---|
1 |
Gradient-based Learning Applied to Document Recognition |
LeNet |
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2 |
ImageNet Classification with Deep Convolutional |
AlexNet |
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3 |
Visualizing and Understanding Convolutional Networks |
ZFNet |
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4 |
VERY DEEP CONVOLUTIONAL |
VGG |
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5 |
Going deeper with convolutions |
GoogleNet,Inceptionv1 |
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6 |
Batch Normalization-Accelerating Deep Network Training b |
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7 |
Rethinking the Inception Architecture for Computer Vision |
Inceptionv3 |
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8 |
Inception-v4:Inception-ResNet and the Impact of Residual Connections on Learning |
Inception-v4 |
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9 |
Xception:Deep Learning with Depthwise Separable Convolutions |
Xception |
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10 |
Deep Residual Learning for Image Recognition |
ResNet |
||||
11 |
Aggregated Residual Transformations for Deep Neural Networks |
ResNeXt |
||||
12 |
Densely Connected Convolutional Networks |
DenseNet |
||||
13 |
Learning Transferable Architectures for Scalable Image Recognition |
NASNet-A |
||||
14 |
MobileNets-Efficient Convolutional Neural Networks for Mobile Vision |
SENet |
||||
15 |
MobileNets- Efficient Convolutional Neural Networks for Mobile Vision |
MobileNets-v1 |
||||
16 |
MobileNetV2:Inverted Residuals and Linear Bottlenecks |
MobileNets-v2 |
||||
17 |
Searching for MobileNetV3 |
MobileNets-v3 |
||||
18 |
ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile |
ShuffleNet |
||||
19 |
ShuffleNet V2:Practical Guidelines for Efficient |
ShuffleNet-v2 |
||||
20 |
Bag of Tricks for Image Classification with Convolutional Neural Networks |
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21 |
EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks |
EfficientNet |
||||
22 |
EfficientNetV2:Smaller Models and Faster Training |
EfficientNet-v2 |
||||
23 |
CSPNET-A NEW BACKBONE THAT CAN ENHANCE LEARNING |
CSPNET-A |
||||
24 |
High-Performance Large-Scale Image Recognition Without Normalization |
NFNets |
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25 |
AN IMAGE IS WORTH 16X16 WORDS-T RANSFORMERS FOR I MAGE R ECOGNITION AT S CALE |
Vision Transformer |
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26 |
Training data-efficient image transformers |
DeiT |
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27 |
Swin Transformer-Hierarchical Vision Transformer using Shifted Windows |
Swin Transformer |
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