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Evolving Neural Style Transfer Blends

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Artificial Intelligence in Music, Sound, Art and Design (EvoMUSART 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12693))

Abstract

Neural style transfer is an image filtering technique used in both digital art practice and commercial software. We investigate blending the styles afforded by neural models via interpolation and overlaying different stylisations. In order to produce preset stylisation filters for the development of a casual creator app, we experiment with various MAP-Elites quality/diversity approaches to evolving style transfer blends with particular properties, while maintaining diversity in the population.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful suggestions.

Author information

Authors and Affiliations

  1. SensiLab, Monash University, Melbourne, Australia

    Simon Colton

  2. Game AI Group, EECS, Queen Mary University of London, London, UK

    Simon Colton

Authors
  1. Simon Colton
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Correspondence to Simon Colton .

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Editors and Affiliations

  1. University of A Coruña, A Coruña, Spain

    Juan Romero

  2. University of Coimbra, Coimbra, Portugal

    Tiago Martins

  3. University of A Coruña, A Coruña, Spain

    Nereida Rodríguez-Fernández

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Colton, S. (2021). Evolving Neural Style Transfer Blends. In: Romero, J., Martins, T., Rodríguez-Fernández, N. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2021. Lecture Notes in Computer Science(), vol 12693. Springer, Cham. https://doi.org/10.1007/978-3-030-72914-1_5

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  • DOI: https://doi.org/10.1007/978-3-030-72914-1_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72913-4

  • Online ISBN: 978-3-030-72914-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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