Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-笔记
本文以论文Suggestive Annotation: A deep active learning frame work for biomedical image segmentation为基础，使用量化技术。 1.对具有高度表达性的训练样本使用suggestive annotation进行量化； 2.为了更高的精确度，使用量化对网络进行训练； 思路就是首先改善数据，然后使用改善的数据训练网络。
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相关文章
- 1. Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation
- 2. U-Net: Convolutional Networks for Biomedical Image Segmentation
- 3. [U-Net] U-Net: Convolutional Networks for Biomedical Image Segmentation
- 4. 【论文笔记】Fully Convolutional Networks for Semantic Segmentation
- 5. 学习笔记：Fully Convolutional Networks for Segmantic Segmentation
- 6. U-Net: Convolutional Networks for Biomedical Image Segmentation翻译
- 7. [U-Net]Convolutional Networks for Biomedical Image Segmentation
- 8. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 9. [论文笔记]：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 10. U-Net-论文笔记-理解:U-Net: Convolutional Networks for Biomedical Image Segmentation