1 Ocak 2021 Konferans bildirisi Açık Erişim

Gokay, Dilara; Simsar, Enis; Atici, Efehan; Ahmetoglu, Alper; Yuksel, Atif Emre; Yanardag, Pinar

Dublin Core

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  <dc:creator>Gokay, Dilara</dc:creator>
  <dc:creator>Simsar, Enis</dc:creator>
  <dc:creator>Atici, Efehan</dc:creator>
  <dc:creator>Ahmetoglu, Alper</dc:creator>
  <dc:creator>Yuksel, Atif Emre</dc:creator>
  <dc:creator>Yanardag, Pinar</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>In this paper, we propose a graph-based image-to-image translation framework for generating images. We use rich data collected from the popular creativity platform Artbreeder', where users interpolate multiple GAN-generated images to create artworks. This unique approach of creating new images leads to a tree-like structure where one can track historical data about the creation of a particular image. Inspired by this structure, we propose a novel graph-to-image translation model called Graph2Pix, which takes a graph and corresponding images as input and generates a single image as output. Our experiments show that Graph2Pix is able to outperform several image-to-image translation frameworks on benchmark metrics, including LPIPS (with a 25% improvement) and human perception studies (n = 60), where users preferred the images generated by our method 81.5% of the time.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/237762</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:237762</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:title>Graph2Pix: A Graph-Based Image to Image Translation Framework</dc:title>
  <dc:type>info:eu-repo/semantics/conferencePaper</dc:type>
  <dc:type>publication-conferencepaper</dc:type>
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