论文：Rich feature hierarchies for accurate object detection and semantic segmentation 个人笔记
这篇论文是2014 CVPR上的，是本人看的目标检测领域的第一篇论文，当时看的时候有很多不太理解的地方~ 1.动机 2.贡献点 优势： 1.共享。CNN对于每个分类的参数都是共享的 2.低维。与其他方法相比，4096维的特征向量算是很小的了 特点： 1.可视化学习 2.经过fine-tunning卷积层提取出来的特征具有普遍性，而全连接层的有针对性，可用于迁移学习 3.网络架构采用O-Net 3.
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相关文章
- 1. Rich feature hierarchies for accurate object detection and semantic segmentation
- 2. 论文笔记：Rich feature hierarchies for accurate object detection and semantic segmentation
- 3. 【论文阅读笔记】Rich feature hierarchies for accurate object detection and semantic segmentation
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- 5. Rich feature hierarchies for accurate object detection and semantic segmentation论文细读
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- 7. R-CNN（Rich feature hierarchies for accurate object detection and semantic segmentation）论文理解...
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- 9. 论文解读：R-CNN《Rich feature hierarchies for accurate object detection and semantic segmentation》
- 10. 【论文阅读】Rich feature hierarchies for accurate object detection and semantic segmentation