Image-to-Image Translation with Conditional Adversarial Networks

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 app...

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Vydané v:	2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) s. 5967 - 5976
Hlavní autori:	Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, Efros, Alexei A.
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2017
Predmet:	Force Gallium nitride Generators Image edge detection Image resolution Training
ISSN:	1063-6919, 1063-6919
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Popis
Shrnutí:	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. Moreover, since the release of the pi×2pi× software associated with this paper, hundreds of twitter users have posted their own artistic experiments using our system. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without handengineering our loss functions either.
ISSN:	1063-6919 1063-6919
DOI:	10.1109/CVPR.2017.632