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    Image Embedding into Generative Adversarial Networks

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    RameenAbdalThesis.pdf
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    Type
    Thesis
    Authors
    Abdal, Rameen cc
    Advisors
    Wonka, Peter cc
    Committee members
    Hadwiger, Markus cc
    Ghanem, Bernard cc
    Program
    Computer Science
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2020-04-14
    Permanent link to this record
    http://hdl.handle.net/10754/662516
    
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    Abstract
    We propose an e cient algorithm to embed a given image into the latent space of StyleGAN. This embedding enables semantic image editing operations that can be applied to existing photographs. Taking the StyleGAN trained on the FFHQ dataset as an example, we show results for image morphing, style transfer, and expression transfer. Studying the results of the embedding algorithm provides valuable insights into the structure of the StyleGAN latent space. We propose a set of experiments to test what class of images can be embedded, how they are embedded, what latent space is suitable for embedding, and if the embedding is semantically meaningful.
    Citation
    Abdal, R. (2020). Image Embedding into Generative Adversarial Networks. KAUST Research Repository. https://doi.org/10.25781/KAUST-4NH5S
    DOI
    10.25781/KAUST-4NH5S
    ae974a485f413a2113503eed53cd6c53
    10.25781/KAUST-4NH5S
    Scopus Count
    Collections
    Theses; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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