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    Personalizing Apparel using Neural Style Transfer

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    Date_umbc_0434M_11672.pdf (2.781Mb)
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    http://hdl.handle.net/11603/15500
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    • UMBC Theses and Dissertations
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    Author/Creator
    Unknown author
    Date
    2017-01-01
    Type of Work
    Text
    thesis
    Department
    Computer Science and Electrical Engineering
    Program
    Computer Science
    Rights
    This item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please see http://aok.lib.umbc.edu/specoll/repro.php or contact Special Collections at speccoll(at)umbc.edu
    Distribution Rights granted to UMBC by the author.
    Subjects
    Convolutional Neural Networks
    Fashion
    Neural Networks
    Personalization
    Style Transfer
    Texture Synthesis
    Abstract
    Convolutional Neural Networks have been highly successful in performing a set of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. Gatys et al. (Gatys, Ecker, & Bethge 2015b) show how the artistic style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this thesis, the neural style transfer method is applied to fashion to synthesize custom clothes. We create a personalization model that is able to generate new custom clothes based on a user?s preference and by learning the user?s fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how they align with the user?s fashion style.


    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
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    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


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    Albin O. Kuhn Library & Gallery
    University of Maryland, Baltimore County
    1000 Hilltop Circle
    Baltimore, MD 21250
    www.umbc.edu/scholarworks

    Contact information:
    Email: scholarworks-group@umbc.edu
    Phone: 410-455-3544


    If you wish to submit a copyright complaint or withdrawal request, please email mdsoar-help@umd.edu.