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Author (up) Yaxing Wang; Abel Gonzalez-Garcia; Joost Van de Weijer; Luis Herranz edit   pdf
url  openurl
  Title SDIT: Scalable and Diverse Cross-domain Image Translation Type Conference Article
  Year 2019 Publication 27th ACM International Conference on Multimedia Abbreviated Journal  
  Volume Issue Pages 1267–1276  
  Keywords  
  Abstract Recently, image-to-image translation research has witnessed remarkable progress. Although current approaches successfully generate diverse outputs or perform scalable image transfer, these properties have not been combined into a single method. To address this limitation, we propose SDIT: Scalable and Diverse image-to-image translation. These properties are combined into a single generator. The diversity is determined by a latent variable which is randomly sampled from a normal distribution. The scalability is obtained by conditioning the network on the domain attributes. Additionally, we also exploit an attention mechanism that permits the generator to focus on the domain-specific attribute. We empirically demonstrate the performance of the proposed method on face mapping and other datasets beyond faces.  
  Address Nice; Francia; October 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference ACM-MM  
  Notes LAMP; 600.106; 600.109; 600.141; 600.120 Approved no  
  Call Number Admin @ si @ WGW2019 Serial 3363  
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