Deep Learning of Individual Aesthetics

McCormack, Jon and Lomas, Andy. 2021. Deep Learning of Individual Aesthetics. Neural Computing and Applications, 33(1), pp. 3-17. ISSN 0941-0643 [Article]

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Abstract or Description

Accurate evaluation of human aesthetic preferences represents a major challenge for creative evolutionary and generative systems research. Prior work has tended to focus on feature measures of the artefact, such as symmetry, complexity and coherence. However, research models from Psychology suggest that human aesthetic experiences encapsulate factors beyond the artefact, making accurate computational models very difficult to design. The interactive genetic algorithm (IGA) circumvents the problem through human-in-the-loop, subjective evaluation of aesthetics, but is limited due to user fatigue and small population sizes. In this paper we look at how recent advances in deep learning can assist in automating personal aesthetic judgement. Using a leading artist's computer art dataset, we investigate the relationship between image measures, such as complexity, and human aesthetic evaluation. We use dimension reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in a generative system. Convolutional Neural Networks trained on the artist's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings. We integrate this classification and discovery system into a software tool for evolving complex generative art and design.

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This research was supported by an Australian Research Council grant FT170100033 and a Monash University International Research Visitors Collaborative Seed Fund grant.

"“This is a post-peer-review, pre-copyedit version of an article published in Neural Computing and Applications. The final authenticated version is available online at:"


Evolutionary Art, Aesthetics, Aesthetic Measure, Convolutional Neural Networks, Dimension Reduction, Morphogenesis

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17 September 2020Accepted
2 October 2020Published Online
January 2021Published

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Date Deposited:

21 Sep 2020 10:12

Last Modified:

02 Oct 2021 01:26

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Yes, this version has been peer-reviewed.


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