Deep learning of individual aesthetics
@article{Mccormack2021DeepLO, title={Deep learning of individual aesthetics}, author={Jon Mccormack and Andy Lomas}, journal={Neural Computing and Applications}, year={2021}, volume={33}, pages={3-17} }
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 circumvents the problem…
10 Citations
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The goal of this research is to automate the process of determining the aesthetic quality of an image numerically. Subsequently, this numerical value will be used in an unsupervised evolutionary…
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The value of direct measures in generative and evolutionary art is discussed, reinforcing recent findings from neuroimaging and psychology which suggest human aesthetic judgement is informed by many extrinsic factors beyond the measurable properties of the object being judged.
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This paper performs a random sampling of genotype space and uses individual artist-assigned evaluations of aesthetic quality to formulate a computable fitness measure specific to the artist and this system, and shows that the quality-diversity search is able to find multiple phenotypes of high aesthetic value.
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