[ICCV2019] SinGAN: Learning a Generative Model from a Single Natural Image
ICCV2019最佳论文Marr奖 作者：Tamar Rott Shaham，Technion（以色列理工） Intro 用一张自然图像训练，测试时可以生成类似该图像的合成图像。 Method 利用金字塔式的多个GAN，每层GAN均引入噪声，对应不同的分辨率。如下图 生成器的具体细节： Results Application Super-Resolution Paint-to-Image Harm
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相关文章
- 1. [ICCV2019 Best Paper Award] SinGAN: Learning a Generative Model from a Single Natural Image
- 2. 论文笔记：SinGAN: Learning a Generative Model from a Single Natural Image
- 3. SinGAN解读: 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 阅读笔记
- 6. 2019-ICCV最佳论文-SinGAN: Learning a Generative Model from a Single Natural Image
- 7. [论文笔记]：SinGAN: Learning a Generative Model from a Single Natural Image
- 8. SinGAN: Learning a Generative Model from a Single Natural Image（ICCV19）
- 9. 论文《Depth Estimation From a Light Field Image Pair With a Generative Model》学习
- 10. A Recursive Restoration Model for Single-Image Super-Resolution(EBRN ICCV2019)