VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
這篇文章重點分析了CNN深度對於其performance的影響，並且提出3×3的filter size可以大幅提升原先的CNN performance。同時，這篇文章提出了VGG的網絡架構，獲得了ImageNet Challenge 2014的冠軍，同時該架構在其他數據集上的泛化能力也很強。 1 ConNet Configuration 2 Classfication framework 2.1
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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