pix2pix Image-to-Image Translation with Conditional Adversarial Networks筆記及代碼實現
1.Introduction GANs在最近兩年得到了廣泛的應用，本文研究的技術很多人就已經提到過，儘管如此，之前的論文都是關注特定的應用，而cGANs作爲圖像轉換的問題的通用方法的有效性仍然是不清楚的，我們的主要貢獻 在於闡釋了cGANs在很多問題上都能產生合理的結果，第二個貢獻在於提出來一個簡單有效的框架，並分析了幾種結構的選擇結果 2.Related work 2.1圖像建模的結構化損失
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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. Image-to-Image Translation with Conditional Adversarial Networks
- 4. Image-to-Image Translation with Conditional Adversarial Networks論文閱讀分享(pix2pix)
- 5. 【翻譯】Image-to-Image Translation with Conditional Adversarial Networks
- 6. Image-to-Image Translation with Conditional Adversarial NetWorks 翻譯
- 7. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 8. 《Image-to-Image Translation with Conditional Adversarial Networks》論文筆記
- 9. 《Image-to-Image Translation with Conditional Adversarial Networks》論文總結
- 10. 全文翻譯&雜記《Image-to-Image Translation with Conditional Adversarial NetWorks》