Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation-筆記
本文以論文Suggestive Annotation: A deep active learning frame work for biomedical image segmentation爲基礎，使用量化技術。 1.對具有高度表達性的訓練樣本使用suggestive annotation進行量化； 2.爲了更高的精確度，使用量化對網絡進行訓練； 思路就是首先改善數據，然後使用改善的數據訓練網絡。
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相關文章
- 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. 【論文筆記】Fully Convolutional Networks for Semantic Segmentation
- 5. 學習筆記：Fully Convolutional Networks for Segmantic Segmentation
- 6. U-Net: Convolutional Networks for Biomedical Image Segmentation翻譯
- 7. [U-Net]Convolutional Networks for Biomedical Image Segmentation
- 8. Fully Convolutional Adaptation Networks for Semantic Segmentation
- 9. [論文筆記]：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 10. U-Net-論文筆記-理解:U-Net: Convolutional Networks for Biomedical Image Segmentation