基本模型
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- Keras Documentation
[2015] VGGNet(16/19) [2]
[2015] GoogleNet [10]
[2016] Inception-v1/v2/v3 [4]
[2016] ResNet [3]
[2017] Xception [1]
[2017] InceptionResNet-(v1/v2)、Inception-v4 [5]
[2017] MobileNet [6]
[2017] DenseNet [7]
[2017] NASNet [8]
[2018] MobileNetV2 [9]
[1] [Xception: Deep Learning with Depthwise Separable Convolutions, CVPR 2017.](./Papers/基本模型/[2017].Xception.(CVPR).pdf)
[2] Very Deep Convolutional Networks for Large-Scale Image Recognition, ICLR 2015.
[3] Deep Residual Learning for Image Recognition, CVPR 2016.
[4] Rethinking the Inception Architecture for Computer Vision, CVPR 2016.
[5] Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning, AAAI 2017.
[6] MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications, arXiv 2017.
[7] Densely Connected Convolutional Networks, CVPR 2017.
[8] Learning Transferable Architectures for Scalable Image Recognition, arXiv 2017.
[9] MobileNetV2: Inverted Residuals and Linear Bottlenecks, CVPR 2018.
- CSDN博客