![]() ![]() We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. In each case we use the same architecture and objective, simply training on different data. Image-to-Image Translation with Conditional Adversarial NetsĮxample results on several image-to-image translation problems. Image-to-Image Translation with Conditional Adversarial Networks
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