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    Towards Diverse Anime Face Generation: Active Label Completion and Style Feature Network

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    Date
    2019
    Author
    Li, Hongyu ORCID
    Han, Tianqi ORCID
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    Abstract
    It is interesting to use an anime face as personal virtual image to replace the traditional sequence code. To generate diverse anime faces, this paper proposes a style-gender based anime GAN (SGA-GAN), where the gender is directly conditioned to ensure the gender differentiation, and style features serve as a condition to guarantee the style diversity. To extract style features, we train a style feature network (SFN) as a multi-task classifier to simultaneously fulfill gender classification, style classification, and image quality estimation. To make full use of available data, partly labeled or unlabeled, during the SFN training, we propose a label completion method to actively complete the missing gender or style labels. The active label completion is essentially a weakly-supervised learning process through ensembling three distinct classifiers to improve the generalization capability. Experiments verify that the active label completion can improve the model accuracy and the style feature as a condition can make better the diversity of generated anime faces.
    BibTeX
    @inproceedings {10.2312:egs.20191016,
    booktitle = {Eurographics 2019 - Short Papers},
    editor = {Cignoni, Paolo and Miguel, Eder},
    title = {{Towards Diverse Anime Face Generation: Active Label Completion and Style Feature Network}},
    author = {Li, Hongyu and Han, Tianqi},
    year = {2019},
    publisher = {The Eurographics Association},
    ISSN = {1017-4656},
    DOI = {10.2312/egs.20191016}
    }
    URI
    https://doi.org/10.2312/egs.20191016
    https://diglib.eg.org:443/handle/10.2312/egs20191016
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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA