SinGAN：Learning a Generative Model from a Single Natural Image
SinGAN：Learning a Generative Model from a Single Natural Image 作者：Tamar Rott Shaham，Tali Dekel，Tomer Michaeli（以色列理工學院，Google Research） 這篇論文提出了一種可以從單幅自然圖像學習的非條件生成模型–SinGAN，能夠捕捉圖像的內部塊分佈信息，生成具有相同視覺內容的高質量
相關文章
- 1. 論文筆記：SinGAN: Learning a Generative Model from a Single Natural Image
- 2. SinGAN: Learning a Generative Model from a Single Natural Image 閱讀筆記
- 3. SinGAN解讀: Learning a Generative Model from a Single Natural Image
- 4. 2019-ICCV最佳論文-SinGAN: Learning a Generative Model from a Single Natural Image
- 5. [論文筆記]：SinGAN: Learning a Generative Model from a Single Natural Image
- 6. [ICCV2019 Best Paper Award] SinGAN: Learning a Generative Model from a Single Natural Image
- 7. [ICCV2019] 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《Depth Estimation From a Light Field Image Pair With a Generative Model》學習
- 10. 3D Face Reconstruction from a Single Image
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相關文章
- 1. 論文筆記：SinGAN: Learning a Generative Model from a Single Natural Image
- 2. SinGAN: Learning a Generative Model from a Single Natural Image 閱讀筆記
- 3. SinGAN解讀: Learning a Generative Model from a Single Natural Image
- 4. 2019-ICCV最佳論文-SinGAN: Learning a Generative Model from a Single Natural Image
- 5. [論文筆記]：SinGAN: Learning a Generative Model from a Single Natural Image
- 6. [ICCV2019 Best Paper Award] SinGAN: Learning a Generative Model from a Single Natural Image
- 7. [ICCV2019] 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《Depth Estimation From a Light Field Image Pair With a Generative Model》學習
- 10. 3D Face Reconstruction from a Single Image