@InProceedings{CáceresCondCháv:2019:ExDoCr,
               author = "C{\'a}ceres, Jorge Roberto L{\'o}pez and Condori, Manasses 
                         Antoni Mauricio and Ch{\'a}vez, Guillermo C{\'a}mara",
          affiliation = "{Universidad Cat{\'o}lica San Pablo} and {Universidad 
                         Cat{\'o}lica San Pablo} and {Federal University of Ouro Preto}",
                title = "Exploring Double Cross Cyclic Interpolation in Unpaired 
                         Image-to-Image Translation",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Oliveira, Luciano Rebou{\c{c}}as de and Sarder, Pinaki and Lage, 
                         Marcos and Sadlo, Filip",
         organization = "Conference on Graphics, Patterns and Images, 32. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "Unpaired Image-to-Image Translation, Latent Space Interpolation, 
                         Cross-domain Model.",
             abstract = "The unpaired image-to-image translation consists of transferring a 
                         sample \$a\$ in the domain \$A\$ to an analog sample \$b\$ 
                         in the domain \$B\$ without intensive pixel-to-pixel 
                         supervision. The current vision focuses on learning a generative 
                         function that maps both domains but ignoring the latent 
                         information, although its exploration is not explicit supervision. 
                         This paper proposes a cross-domain GAN-based model to achieve a 
                         bi-directional translation guided by latent space supervision. The 
                         proposed architecture provides a double-loop cyclic reconstruction 
                         loss in an exchangeable training adopted to reduce mode collapse 
                         and enhance local details. Our proposal has outstanding results in 
                         visual quality, stability, and pixel-level segmentation metrics 
                         over different public datasets.",
  conference-location = "Rio de Janeiro, RJ, Brazil",
      conference-year = "28-31 Oct. 2019",
                  doi = "10.1109/SIBGRAPI.2019.00025",
                  url = "http://dx.doi.org/10.1109/SIBGRAPI.2019.00025",
             language = "en",
                  ibi = "8JMKD3MGPEW34M/3U3K5TH",
                  url = "http://urlib.net/ibi/8JMKD3MGPEW34M/3U3K5TH",
           targetfile = "Sibgrapi19_CycleGAN.pdf",
        urlaccessdate = "2024, Apr. 28"
}