Understanding Aesthetic Evaluation using Deep Learning
This work addresses the problem of user fatigue and limited search in interactive genetic algorithms for artists and designers, though it is incremental as it builds on existing deep learning techniques.
The paper tackled the bottleneck of aesthetic evaluation in evolutionary art systems by using deep learning to automate personal aesthetic judgment, resulting in a method that suggests new possibilities based on prior user evaluations.
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.