CNN結構模型一句話概述：從LeNet到ShuffleNet

• 2019 年 11 月 24 日
• 筆記

由簡入繁，由繁入簡。已瘋……

1. LeNet：Gradient based learning applied to document recognition
2. AlexNet：ImageNet Classification with Deep Convolutional Neural Networks
3. ZFNet：Visualizing and understanding convolutional networks
4. VGGNet：Very deep convolutional networks for large-scale image recognition
5. NiN：Network in network
6. GoogLeNet：Going deeper with convolutions
7. Inception-v3：Rethinking the inception architecture for computer vision
8. ResNet：Deep residual learning for image recognition
9. Stochastic_Depth：Deep networks with stochastic depth
10. WResNet：Weighted residuals for very deep networks
11. Inception-ResNet：Inception-v4,inception-resnet and the impact of residual connections on learning
12. Fractalnet：Ultra-deep neural networks without residuals
13. WRN：Wide residual networks
14. ResNeXt：Aggregated Residual Transformations for Deep Neural Networks
15. DenseNet：Densely connected convolutional networks
16. PyramidNet：Deep Pyramidal Residual Networks
17. DPN：Dual Path Networks
18. SqueezeNet：AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
19. MobileNets：Efficient Convolutional Neural Networks for Mobile Vision Applications
20. ShuffleNet：An Extremely Efficient Convolutional Neural Network for Mobile Devices
21. LeNet：基於漸變的學習應用於文檔識別
22. AlexNet：具有深卷積神經網絡的ImageNet分類
23. ZFNet：可視化和理解卷積網絡
24. VGGNet：用於大規模圖像識別的非常深的卷積網絡
25. NiN：網絡中的網絡
26. GoogLeNet：捲入更深入
27. Inception-v3：重新思考計算機視覺的初始架構
28. ResNet：圖像識別的深度殘差學習
29. Stochastic_Depth：具有隨機深度的深層網絡
30. WResNet：非常深的網絡的加權殘差
31. Inception-ResNet：Inception-v4，inception-resnet以及剩餘連接對學習的影響
32. Fractalnet：沒有殘差的超深層神經網絡
33. WRN：寬殘留網絡
34. ResNeXt：深層神經網絡的聚合殘差變換
35. DenseNet：密集連接的卷積網絡
36. PyramidNet：深金字塔殘留網絡
37. DPN：雙路徑網絡
38. SqueezeNet：AlexNet級準確度，參數減少50倍，模型尺寸小於0.5MB
39. MobileNets：用於移動視覺應用的高效卷積神經網絡
40. ShuffleNet：移動設備極高效的卷積神經網絡

原創文章，轉載請註明： 轉載自URl-team

本文鏈接地址: CNN結構模型一句話概述：從LeNet到ShuffleNet