GAN系列：Image-to-Image Translation with Conditional Adversarial Networks
Abstract 1. image-to-image trainslation 存在的兩個問題： 1）many-to-one:將照片映射爲edges/segments/semantic labels 2）one-to-many:將labels/sparse user inputs映射爲真實圖片 一般的做法是predict pixels form pixels；作者的創新點爲develop a co
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相關文章
- 1. pix2pix（2017）：Image-to-Image Translation with Conditional Adversarial Networks
- 2. Image-to-Image Translation with Conditional Adversarial Networks筆記
- 3. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 4. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯
- 5. Image-to-Image Translation with Conditional Adversarial Networks
- 6. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 7. 論文之Image-to-Image Translation with Conditional Adversarial Networks 2
- 8. pix2pix Image-to-Image Translation with Conditional Adversarial Networks筆記及代碼實現
- 9. 全文翻譯&雜記《Image-to-Image Translation with Conditional Adversarial NetWorks》
- 10. Image-to-Image Translation with Conditional Adversarial Networks 論文翻譯