图片范式转换 使用条件式对抗网络

https://phillipi.github.io/pix2pix/

Amazing

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

我们将条件对话网络作为图像到图像翻译问题的通用解决方案进行研究。 这些网络不仅学习从输入图像到输出图像的映射，而且还学习一个损失函数来训练该映射。 这使得可能对传统上需要非常不同的损失配方的问题应用相同的通用方法。 我们证明，这种方法在从标签贴图合成照片，从边缘地图重建对象，以及着色图像以及其他任务中是有效的。 作为一个社区，我们不再手工设计我们的映射功能，而且这项工作表明我们可以在没有手工制作我们的损失函数的情况下实现合理的结果。

视频介绍： https://youtu.be/u7kQ5lNfUfg

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