論文：Rich feature hierarchies for accurate object detection and semantic segmentation 個人筆記
這篇論文是2014 CVPR上的，是本人看的目標檢測領域的第一篇論文，當時看的時候有很多不太理解的地方~ 1.動機 2.貢獻點 優勢： 1.共享。CNN對於每個分類的參數都是共享的 2.低維。與其他方法相比，4096維的特徵向量算是很小的了 特點： 1.可視化學習 2.經過fine-tunning卷積層提取出來的特徵具有普遍性，而全連接層的有針對性，可用於遷移學習 3.網絡架構採用O-Net 3.
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相關文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 【論文閱讀筆記】Rich feature hierarchies for accurate object detection and semantic segmentation
- 4. 論文筆記：Rich feature hierarchies for accurate object detection and semantic segmentation(R-CNN)
- 5. Rich feature hierarchies for accurate object detection and semantic segmentation論文細讀
- 6. 【論文學習】Rich feature hierarchies for accurate object detection and semantic segmentation
- 7. R-CNN（Rich feature hierarchies for accurate object detection and semantic segmentation）論文理解...
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- 9. 論文解讀：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 10. 【論文閱讀】Rich feature hierarchies for accurate object detection and semantic segmentation