Backbone发展与语义分割网络发展
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Backbone发展与语义分割网络发展
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整理如下（按照arxiv上面時間線的預印版本來整理）:
Backbone（基礎網絡,也可以理解為分類網絡）：
Backbone可以塞入UNET作為使用。
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| 年代 | 網絡名稱與代碼 | 論文名稱 |
| 1989 | LeNet | Backpropagation Applied to Handwritten Zip Code Recognition |
| 1995 | LeNet4, Boosted LeNet4, LeNet5 | Comparison of learning algorithms for handwritten digit recognition |
| 1998 | LeNet4, Boosted LeNet4, LeNet5 | GradientBased Learning Applied to Document Recognition |
| 2012 | AlexNet | ImageNet Classification with Deep Convolutional Neural Networks |
| 2013-12-16 | NiN | Network In Network |
| 2014-9-4 | VGG16-VGG19 | VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION |
| 2014-9-17 | Inception v1 (又稱為GoogLeNet) | Going Deeper with Convolutions |
| 2015-2-6 | MSRANet | Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification |
| 2015-12-2 | Inception v2 (又稱為GoogLeNet) | Rethinking the Inception Architecture for Computer Vision |
| 2015-12-10 | ResNet | Deep Residual Learning for Image Recognition |
| 2016-2-23 | Inception v4 (又稱為GoogLeNet) | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning |
| 2016-2-24 | SqueezeNet | SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size |
| 2016-3-16 | ResNet v2 | Identity Mappings in Deep Residual Networks |
| 2016-5-23 | Wide ResNet | Wide Residual Networks |
| 2016-8-25 | DenseNet | Densely Connected Convolutional Networks |
| 2016-10-7 | Inception v3 (又稱為GoogLeNet) | Xception: Deep Learning with Depthwise Separable Convolutions |
| 2016-11-16 | ResNext | Aggregated Residual Transformations for Deep Neural Networks |
| 2017-4-17 | MobileNet | MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications |
| 2017-7-4 | ShuffleNet | ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices |
| 2017-7-6 | DPNet | Dual Path Networks |
| 2017-9-5 | SENet: SE-ResNet，SE-ResNext | Squeeze-and-Excitation Networks |
| 2017-10-26 | Capsules | Dynamic Routing Between Capsules |
| 2018-1-13 | MobileNet v2 | MobileNetV2: Inverted Residuals and Linear Bottlenecks |
| 2018-5-23 | SqueezeNext | SqueezeNext: Hardware-Aware Neural Network Design |
| 2018-7-30 | ShuffleNet V2 | ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design |
| 2018-7-31 | NasNet | MnasNet: Platform-Aware Neural Architecture Search for Mobile |
| 2019-1-24 | AutoShuffleNet | AutoShuffleNet: Learning Permutation Matrices via an Exact Lipschitz Continuous Penalty in Deep Convolutional Neural Networks |
| 2019-5-6 | MobileNet v3 | Searching for MobileNetV3 |
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語義分割網絡：
| 年代 | 網絡名稱與代碼 | 論文名稱 |
| 2013-11-11 | RCNN | Rich feature hierarchies for accurate object detection and semantic segmentation |
| 2014-6-18 | SPP（目標檢測） | Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition |
| 2014-11-14? | FCN | Fully Convolutional Networks for Semantic Segmentation |
| 2014-12-22 | DeepLab v1 | SEMANTIC IMAGE SEGMENTATION WITH DEEP CONVOLUTIONAL NETS AND FULLY CONNECTED CRFS |
| 2015-5-18 | UNET | U-Net: Convolutional Networks for Biomedical Image Segmentation |
| 2015-6-4 | Faster R-CNN（里面提出了RPN） | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks |
| 2015-6-8 | YOLO v1 | You Only Look Once: Unified, Real-Time Object Detection |
| 2015-11-9 | SegNet | Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding |
| 2015-12月 | Fast R-CNN | Fast R-CNN |
| 2015-12-8 | SSD | SSD: Single Shot MultiBox Detector |
| 2016-6-2 | DeepLab v2 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs |
| 2016-11-20 | RefineNet | RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation |
| 2016-12-4 | PSPNet | Pyramid Scene Parsing Network |
| 2016-12-9 | FPN | Feature Pyramid Networks for Object Detection |
| 2016-12-25 | YOLO v2 | YOLO9000: Better, Faster, Stronger |
| 2017-3-20 | Mask-RCNN | Mask R-CNN |
| 2017-6-13 | DeepLab v3 | Rethinking Atrous Convolution for Semantic Image Segmentation |
| 2018-4-8 | YOLO v3 | YOLOv3: An Incremental Improvement |
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各種網絡的實現框架可以參考[1],
各種網絡的綜述可以參考[2]
想看個大概的可以翻閱[3]
[4]的內容很有意思，可以看下：
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[1]https://blog.csdn.net/helloworld_Fly/article/details/80306117
[2]https://arxiv.org/pdf/1704.06857.pdf
[3]https://blog.csdn.net/qq_20084101/article/details/80432960
[4]https://www.jiqizhixin.com/articles/092301
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