Non-local U-Nets for Biomedical Image Segmentation
code tensorflow Non-local 由三個部分組成： U-Net、Residual Blocks、Global Aggregation Block U-Net 作者將原始u-net的concatenation -> sum的原因： 求和不會增加特徵圖的數量，因此會減少下一層中可訓練參數的數量。 具有求和的skip connection可以被視爲遠程殘差連接，可以促進模型的訓練。 最
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相關文章
- 1. Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation
- 2. [U-Net] U-Net: Convolutional Networks for Biomedical Image Segmentation
- 3. U-Net: Convolutional Networks for Biomedical Image Segmentation翻譯
- 4. U-Net: Convolutional Networks for Biomedical Image Segmentation
- 5. Context-Aware U-Net for Biomedical Image Segmentation
- 6. [U-Net]Convolutional Networks for Biomedical Image Segmentation
- 7. 【論文學習】Non-local U-Nets for Biomedical Image Segmentation
- 8. 【Semantic Segmentation】U-Net: Convolutional Networks for Biomedical Image Segmentation 論文解析(轉)...
- 9. 論文解讀：Adaptive Nonlocal Random Walks for Image Superpixel Segmentation
- 10. 論文筆記：U-Net: Convolutional Networks for Biomedical Image Segmentation