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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Veröffentlicht in:2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) S. 5967 - 5976
Hauptverfasser: Isola, Phillip, Jun-Yan Zhu, Tinghui Zhou, Efros, Alexei A.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2017
Schlagworte:
Force
Gallium nitride
Generators
Image edge detection
Image resolution
Training
ISSN:1063-6919, 1063-6919
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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 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.
AbstractList 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.
Author Tinghui Zhou
Jun-Yan Zhu
Efros, Alexei A.
Isola, Phillip
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  surname: Jun-Yan Zhu
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  surname: Tinghui Zhou
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  givenname: Alexei A.
  surname: Efros
  fullname: Efros, Alexei A.
  organization: Berkeley AI Res. (BAIR) Lab., UC Berkeley, Berkeley, CA, USA
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Snippet We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping...
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StartPage 5967
SubjectTerms Force
Gallium nitride
Generators
Image edge detection
Image resolution
Training
Title Image-to-Image Translation with Conditional Adversarial Networks
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