VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
VGG和AlexNet都是很經典的模型。而VGG也是在AlexNet的基礎之上進行改進的。AlexNet每層僅僅含有一個Convolution層，filter的大小7x7；VGG每層含有2~4個Conv層，filter的大小是3x3（能感受空間最小的filter）。 個人認爲，整個文章相比於AlexNet最大的亮點就是更深的卷積層，以及最小的filter。 在本文中，作者討論了ConvNet架構設
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相關文章
- 1. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 2. VGG——Very deep convolutional networks for large-scale image recognition
- 3. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 4. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 5. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 6. VGG: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 7. VGGNet論文翻譯—Very Deep Convolutional Networks for Large-Scale Image Recognition
- 8. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION論文翻譯
- 9. 盞茶論文CV篇(二) Very Deep Convolutional Networks for Large-Scale Image Recognition
- 10. [VGG論文詳解](2014)Very Deep Convolutional Networks For Large-Scale Image Recognition