Understanding Aesthetic Evaluation using Deep Learning
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McCormack, Jon and Lomas, Andy. 2020. 'Understanding Aesthetic Evaluation using Deep Learning'. In: evoMUSART. Seville, Spain 15-17 April 2020. [Conference or Workshop Item]
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Text (Understanding Aesthetic Evaluation using Deep Learning)
UnderstandingAestheticEvaluationUsingDeepLearning_210120.pdf - Accepted Version Available under License Creative Commons Attribution Non-commercial. Download (4MB) | Preview |
Abstract or Description
A bottleneck in any evolutionary art system is aesthetic evaluation. Many different methods have been proposed to automate the evaluation of aesthetics, including measures of symmetry, coherence, complexity, contrast and grouping. The interactive genetic algorithm (IGA) relies on human-in-the-loop, subjective evaluation of aesthetics, but limits possibilities for large search 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 use dimensionality reduction methods to visualise both genotype and phenotype space in order to support the exploration of new territory in any generative system. Convolutional Neural Networks trained on the user's prior aesthetic evaluations are used to suggest new possibilities similar or between known high quality genotype-phenotype mappings.
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Item Type: |
Conference or Workshop Item (Paper) |
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| Keywords: |
Evolutionary Art, Aesthetics, Aesthetic Measure, Convolutional Neural Networks, Dimension Reduction, Morphogenesis |
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| Event Location: |
Seville, Spain |
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| Date range: |
15-17 April 2020 |
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| Item ID: |
28095 |
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| Date Deposited: |
21 Jan 2020 11:40 |
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| Last Modified: |
10 Jun 2021 05:46 |
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