图像识别3-VGGNet-very deep convolutional Network for large-scale image recognition
文章重点： 1.使用更小的3*3的卷积核与更深的网络（16-19层）获得了比ZFNet和AlexNet更好的效果，并且作者测试了不同的数据集证明了该网络具有很好的泛化性，在其他的测试集上也表现出了很好的性能。 2.作者对比了5种深度的卷积网络证明深度越深，在训练数据越大的情况下效果越好。 3.使用了多尺度缩放数据增强技术，证明效果比单尺度数据增强效果提高接近%1. 4.网络的配置：在训练阶段，卷积
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相关文章
- 1. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 2. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 3. VGG: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 4. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 5. VGGNet：Very Deep Convolutional Networks for Large-Scale Image Recognition
- 6. VGG——Very deep convolutional networks for large-scale image recognition
- 7. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 8. VGG——Very Deep Convolution Network for Large-Scale Image Recognition
- 9. 总结Deep Convolutional Neural Network for Image Deconvolution
- 10. ResNet--Deep Residual Learning for Image Recognition