《U-Net: Convolutional Networks for Biomedical Image Segmentation》论文阅读
一、摘要 人们普遍认为，成功的深层网络培训需要数千个带注释的培训样本。在本文中，我们提出了一种网络和培训策略，它依赖于 数据增强的强大使用来更有效地使用可用的带注释的样本。该体系结构由捕获上下文的收缩路径和支持精确本地化的对称扩展路 径组成。In this paper, we present a network and training strategy that relies on
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相关文章
- 1. U-Net: Convolutional Networks for Biomedical Image Segmentation------2015CVPR UNet论文解读
- 2. [论文阅读笔记]U-Net: Convolutional Networks for Biomedical Image Segmentation
- 3. 『 论文阅读』U-Net Convolutional Networks for Biomedical Image Segmentation
- 4. [U-Net]Convolutional Networks for Biomedical Image Segmentation
- 5. [U-Net] U-Net: Convolutional Networks for Biomedical Image Segmentation
- 6. U-Net: Convolutional Networks for Biomedical Image Segmentation
- 7. 论文笔记：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 8. 论文精读及分析：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 9. U-Net: Convolutional Networks for Biomedical Image Segmentation（论文笔记）
- 10. 【Semantic Segmentation】U-Net: Convolutional Networks for Biomedical Image Segmentation 论文解析(转)...