VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
本文參考網上多個對論文的理解. 1.VGG 重要貢獻： 展示出網絡的深度是算法優良性能的關鍵部分 2.VGG網絡的改進點： a.使用了更小的3*3卷積核，和更深的網絡。兩個3*3卷積核的堆疊相對於5*5卷積核的視野，三個3*3卷積核的堆疊相當於7*7卷積核的視野。這樣一方面可以有更少的參數；另一方面擁有更多的非線性變換，增加了CNN對特徵的學習能力。 b.在VGGNet的卷積結構中，引入1*1的卷
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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