Paper | U-Net: Convolutional Networks for Biomedical Image Segmentation
目錄 故事背景 U-Net 具體結構 損失 數據擴充 發表在2015 MICCAI。原本是一篇醫學圖像分割的論文，但由於U-Net傑出的網絡設計，得到了8k+的引用。 摘要 There is large consent that successful training of deep networks requires many thousand annotated training sample
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- 4. U-Net: Convolutional Networks for Biomedical Image Segmentation------2015CVPR UNet論文解讀
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相關文章
- 1. [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
- 4. U-Net: Convolutional Networks for Biomedical Image Segmentation------2015CVPR UNet論文解讀
- 5. U-Net: Convolutional Networks for Biomedical Image Segmentation翻譯
- 6. 論文筆記：U-Net: Convolutional Networks for Biomedical Image Segmentation
- 7. 醫學圖像分割--U-Net: Convolutional Networks for Biomedical Image Segmentation
- 8. U-Net: Convolutional Networks for Biomedical Image Segmentation（論文筆記）
- 9. 【Semantic Segmentation】U-Net: Convolutional Networks for Biomedical Image Segmentation 論文解析(轉)...
- 10. U-Net-論文筆記-理解:U-Net: Convolutional Networks for Biomedical Image Segmentation