Exploration of semantic label decomposition and dataset size in semantic indoor scenes synthesis via optimized residual generative adversarial networks

HIGHLIGHTS

• who: Hatem Ibrahem and collaborators from the Department of Information and Communication Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju-si, Korea have published the article: Exploration of Semantic Label Decomposition and Dataset Size in Semantic Indoor Scenes Synthesis via Optimized Residual Generative Adversarial Networks, in the Journal: Sensors 2022, 22, 8306. of /2022/
• what: The authors propose a generative adversarial network-based technique to create new artificial indoor scenes using a user-defined semantic segmentation map as an input to define the location shape and category of each object in the . . .
  

   

  

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