Image recognition

Network in network (NIN)

https://arxiv.org/abs/1312.4400

• Global average pooling

• 1*1 convolutional kernel

Very Deep Convolutional Networks for Large-Scale Image Recognition (VGG)

https://arxiv.org/abs/1409.1556

• Simply stacks more convolution layers

• Stack small convolutional kernels to achieve the same "receptive field" as bigger one.

  • Stack two 33 convolutional kernel is equivalent to 55 convolutional kernel

One 55 kernel and stacks two 33 kernels have the same effective receptive field.

Deep Residual Learning for Image Recognition (Resnet)

https://arxiv.org/abs/1512.03385

• Add residual connection

Inception

Xception

Dense net

Squeeze and excitation network

Dual path network

Shuffle network

Squeeze network

ResXt

Fully Convolutional Attention Networks for Fine-Grained Recognition

https://arxiv.org/pdf/1603.06765.pdf

Residual Attention Network for Image Classification

https://arxiv.org/pdf/1704.06904.pdf

https://github.com/tengshaofeng/ResidualAttentionNetwork-pytorch https://github.com/youansheng/AttentionModule