对抗学习-图像生成Gan.zip

几篇gan论文。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 func- tion 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 demon- strate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a commu- nity, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either