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Gokay, Dilara; Simsar, Enis; Atici, Efehan; Ahmetoglu, Alper; Yuksel, Atif Emre; Yanardag, Pinar
{ "@context": "https://schema.org/", "@id": 237762, "@type": "ScholarlyArticle", "creator": [ { "@type": "Person", "affiliation": "Tech Univ Munich, Munich, Germany", "name": "Gokay, Dilara" }, { "@type": "Person", "name": "Simsar, Enis" }, { "@type": "Person", "affiliation": "Bogazici Univ, Bebek, Turkey", "name": "Atici, Efehan" }, { "@type": "Person", "affiliation": "Bogazici Univ, Bebek, Turkey", "name": "Ahmetoglu, Alper" }, { "@type": "Person", "affiliation": "Bogazici Univ, Bebek, Turkey", "name": "Yuksel, Atif Emre" }, { "@type": "Person", "affiliation": "Bogazici Univ, Bebek, Turkey", "name": "Yanardag, Pinar" } ], "datePublished": "2021-01-01", "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.", "headline": "Graph2Pix: A Graph-Based Image to Image Translation Framework", "identifier": 237762, "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", "license": "http://www.opendefinition.org/licenses/cc-by", "name": "Graph2Pix: A Graph-Based Image to Image Translation Framework", "url": "https://aperta.ulakbim.gov.tr/record/237762" }
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