Image-to-Image Translation with Conditional Adversarial Networks

Abstract

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… (More)
DOI: 10.1109/CVPR.2017.632

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@article{Isola2017ImagetoImageTW, title={Image-to-Image Translation with Conditional Adversarial Networks}, author={Phillip Isola and Jun-Yan Zhu and Tinghui Zhou and Alexei A. Efros}, journal={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, year={2017}, pages={5967-5976} }