由简入繁，由繁入简。已疯……
- LeNet：Gradient based learning applied to document recognition
- AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
- ZFNet：Visualizing and understanding convolutional networks
- VGGNet：Very deep convolutional networks for large-scale image recognition
- NiN：Network in network
- GoogLeNet：Going deeper with convolutions
- Inception-v3：Rethinking the inception architecture for computer vision
- ResNet：Deep residual learning for image recognition
- Stochastic_Depth：Deep networks with stochastic depth
- WResNet：Weighted residuals for very deep networks
- Inception-ResNet：Inception-v4,inception-resnet and the impact of residual connections on learning
- Fractalnet：Ultra-deep neural networks without residuals
- WRN：Wide residual networks
- ResNeXt：Aggregated Residual Transformations for Deep Neural Networks
- DenseNet：Densely connected convolutional networks
- PyramidNet：Deep Pyramidal Residual Networks
- DPN：Dual Path Networks
- SqueezeNet：AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
- MobileNets：Efficient Convolutional Neural Networks for Mobile Vision Applications
- ShuffleNet：An Extremely Efficient Convolutional Neural Network for Mobile Devices
- LeNet：基于渐变的学习应用于文档识别
- AlexNet：具有深卷积神经网络的ImageNet分类
- ZFNet：可视化和理解卷积网络
- VGGNet：用于大规模图像识别的非常深的卷积网络
- NiN：网络中的网络
- GoogLeNet：卷入更深入
- Inception-v3：重新思考计算机视觉的初始架构
- ResNet：图像识别的深度残差学习
- Stochastic_Depth：具有随机深度的深层网络
- WResNet：非常深的网络的加权残差
- Inception-ResNet：Inception-v4，inception-resnet以及剩余连接对学习的影响
- Fractalnet：没有残差的超深层神经网络
- WRN：宽残留网络
- ResNeXt：深层神经网络的聚合残差变换
- DenseNet：密集连接的卷积网络
- PyramidNet：深金字塔残留网络
- DPN：双路径网络
- SqueezeNet：AlexNet级准确度，参数减少50倍，模型尺寸小于0.5MB
- MobileNets：用于移动视觉应用的高效卷积神经网络
- ShuffleNet：移动设备极高效的卷积神经网络
原创文章，转载请注明： 转载自URl-team
本文链接地址: CNN结构模型一句话概述：从LeNet到ShuffleNet
