《Very Deep Convolutional Networks for Large-Scale Image Recognition》論文閱讀
這是一篇2015.04的論文。 Abstract 本文探索了神經網絡的深度在大尺寸圖像愛那個識別精度上的影響。我們主要的貢獻是：完整地評估了一個用 3x3 卷積濾波器增加深度的網絡，結果表明，當層數達到 16-19 層的時候，精度會有一個很好的提升。這個發現也使我們團隊在 ImageNet Challenge 2014 獲得了定位第一名，分類第二名的主要原因。我們也證明了我們的模型在其他數據集
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相關文章
- 1. 論文閱讀——VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 2. 論文閱讀：VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION（VGG）
- 3. 論文閱讀筆記--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 4. VGG--VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 5. VGG——Very deep convolutional networks for large-scale image recognition
- 6. Very Deep Convolutional NetWorks for Large-Scale Image Recognition
- 7. VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 8. Very Deep Convolutional Networks for Large-Scale Image Recognition
- 9. VGG: VERY DEEP CONVOLUTIONAL NETWORKS FOR LARGE-SCALE IMAGE RECOGNITION
- 10. very deep convolutional networks for lorge-scale image recognition(VGG)閱讀總結