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    Towards a Principled Evaluation of Likeability for Machine-Generated Art

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    Name:
    V3_Towards a Principled Evaluation of Likeability for Machine-Generated Art.pdf
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    6.436Mb
    Format:
    PDF
    Description:
    Conference Paper
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    Type
    Conference Paper
    Authors
    Coleman, Lia
    Achlioptas, Panos
    Elhoseiny, Mohamed cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Visual Computing Center (VCC)
    Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/662646
    
    Metadata
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    Abstract
    Creativity is a cornerstone of human intelligence and perhaps its most complex aspect. Thus, it is very interesting to understand how AI is already being used by professionals in creative domains like the arts and fashion. Namely, do artists actually like AI-generated “paintings"? In this study we collect and analyze responses on these questions from various contemporary artists and compare them to more naive, crowd-sourced ones. We highlight the importance of considering artists’ opinion when evaluating AI-based art, and present a promising approach for researchers to do this easily.
    Sponsors
    We want to thank the artists who helped us with our research: Brooke Cheng, Dwayne Jones, Joseph Wilk, Luisa Fabrizi, Mark Hernandez, Taís Mauk, Michelle Cheung, Julia Peter, Francisco Rojo, Mathilde Mouw-Rao, Iain Nash, and Achim Koh.
    Publisher
    NeurIPS
    Conference/Event name
    32nd Conference on Neural Information Processing Systems (NeurIPS 2019), Montréal, Canada
    Additional Links
    https://neurips2019creativity.github.io/doc/V3_Towards%20a%20Principled%20Evaluation%20of%20Likeability%20for%20Machine-Generated%20Art.pdf
    Collections
    Conference Papers; Visual Computing Center (VCC); Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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