[ICCV2019] SinGAN: Learning a Generative Model from a Single Natural Image
ICCV2019最佳論文Marr獎 作者：Tamar Rott Shaham，Technion（以色列理工） Intro 用一張自然圖像訓練，測試時可以生成類似該圖像的合成圖像。 Method 利用金字塔式的多個GAN，每層GAN均引入噪聲，對應不同的分辨率。如下圖 生成器的具體細節： Results Application Super-Resolution Paint-to-Image Harm
相關文章
- 1. [ICCV2019 Best Paper Award] 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《Depth Estimation From a Light Field Image Pair With a Generative Model》學習
- 10. A Recursive Restoration Model for Single-Image Super-Resolution(EBRN ICCV2019)
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相關文章
- 1. [ICCV2019 Best Paper Award] 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《Depth Estimation From a Light Field Image Pair With a Generative Model》學習
- 10. A Recursive Restoration Model for Single-Image Super-Resolution(EBRN ICCV2019)