Image-to-Image Translation with Conditional Adversarial Networks论文阅读分享(pix2pix)
一.简介 1.1研究的主要内容 本文主要研究CGANS（条件对生成抗网络）作为图像到图像转换问题的通用解决方案，提出了pix2pix软件，并且证明了该方法可有效地从标签图合成照片，从边缘图重建对象以及为图像着色等任务。 这个图片是展示了一些图像到图像转换的结果 1.2创新点 1.证明在各种问题上，conditional GAN都能产生合理的结果。 2.提出一个足以取得良好结果的简单框架，并分析几种
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相关文章
- 1. pix2pix（2017）：Image-to-Image Translation with Conditional Adversarial Networks
- 2. pix2pix Image-to-Image Translation with Conditional Adversarial Networks笔记及代码实现
- 3. (Pixel2PixelGANs)Image-to-Image translation with conditional adversarial networks
- 4. Image-to-Image Translation with Conditional Adversarial Networks
- 5. Image-to-Image Translation with Conditional Adversarial Networks笔记
- 6. Image-to-Image Translation with Conditional Adversarial NetWorks 翻译
- 7. 【翻译】Image-to-Image Translation with Conditional Adversarial Networks
- 8. 论文阅读：pix2pix and CycleGAN
- 9. 《Image-to-Image Translation with Conditional Adversarial Networks》论文总结
- 10. 《Image-to-Image Translation with Conditional Adversarial Networks》论文笔记