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    Disentangled Image Generation Through Structured Noise Injection

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    Type
    Conference Paper
    Authors
    Alharbi, Yazeed cc
    Wonka, Peter cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2020-08-05
    Preprint Posting Date
    2020-04-26
    Online Publication Date
    2020-08-05
    Print Publication Date
    2020-06
    Permanent link to this record
    http://hdl.handle.net/10754/662872
    
    Metadata
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    Abstract
    We explore different design choices for injecting noise into generative adversarial networks (GANs) with the goal of disentangling the latent space. Instead of traditional approaches, we propose feeding multiple noise codes through separate fully-connected layers respectively. The aim is restricting the influence of each noise code to specific parts of the generated image. We show that disentanglement in the first layer of the generator network leads to disentanglement in the generated image. Through a grid-based structure, we achieve several aspects of disentanglement without complicating the network architecture and without requiring labels. We achieve spatial disentanglement, scale-space disentanglement, and disentanglement of the foreground object from the background style allowing fine-grained control over the generated images. Examples include changing facial expressions in face images, changing beak length in bird images, and changing car dimensions in car images. This empirically leads to better disentanglement scores than state-of-the-art methods on the FFHQ dataset.
    Citation
    Alharbi, Y., & Wonka, P. (2020). Disentangled Image Generation Through Structured Noise Injection. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). doi:10.1109/cvpr42600.2020.00518
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Conference/Event name
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
    ISBN
    978-1-7281-7169-2
    DOI
    10.1109/CVPR42600.2020.00518
    arXiv
    2004.12411
    Additional Links
    https://ieeexplore.ieee.org/document/9157760/
    https://ieeexplore.ieee.org/document/9157760/
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9157760
    ae974a485f413a2113503eed53cd6c53
    10.1109/CVPR42600.2020.00518
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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