[ICCV2019 Best Paper Award] SinGAN: Learning a Generative Model from a Single Natural Image
本篇是剛剛召開的ICCV2019獲得Best Paper Award的一篇文章。 [arxiv] : 鏈接 [homepage] : 鏈接 [code] : github鏈接 [demo動畫] ：youtube鏈接 Too Long; Didn’t Read 本篇文章是一篇關於GAN作圖像生成和圖像編輯的文章。 在GAN領域首次嘗試使用單張圖作爲訓練數據生成保證結構不變而具有多樣性的圖片。（圖1）
相關文章
- 1. [ICCV2019] SinGAN: Learning a Generative Model from a Single Natural Image
- 2. 論文筆記：SinGAN: Learning a Generative Model from a Single Natural Image
- 3. SinGAN解讀: Learning a Generative Model from a Single Natural Image
- 4. SinGAN：Learning a Generative Model from a Single Natural Image
- 5. SinGAN: Learning a Generative Model from a Single Natural Image 閱讀筆記
- 6. 2019-ICCV最佳論文-SinGAN: Learning a Generative Model from a Single Natural Image
- 7. [論文筆記]：SinGAN: Learning a Generative Model from a Single Natural Image
- 8. SinGAN: Learning a Generative Model from a Single Natural Image（ICCV19）
- 9. 論文解讀：ICCV2019 Best paper SinGAN
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相關文章
- 1. [ICCV2019] SinGAN: Learning a Generative Model from a Single Natural Image
- 2. 論文筆記：SinGAN: Learning a Generative Model from a Single Natural Image
- 3. SinGAN解讀: Learning a Generative Model from a Single Natural Image
- 4. SinGAN：Learning a Generative Model from a Single Natural Image
- 5. SinGAN: Learning a Generative Model from a Single Natural Image 閱讀筆記
- 6. 2019-ICCV最佳論文-SinGAN: Learning a Generative Model from a Single Natural Image
- 7. [論文筆記]：SinGAN: Learning a Generative Model from a Single Natural Image
- 8. SinGAN: Learning a Generative Model from a Single Natural Image（ICCV19）
- 9. 論文解讀：ICCV2019 Best paper SinGAN
- 10. GANimation ：Anatomically-aware Facial Animation from a Single Image