RESEARCH

COMPUTER VISION

Neuro-Symbolic Generative Art: A Preliminary Study

September 7, 2020

Abstract

There are two classes of generative art approaches: neural, where a deep model is trained to generate samples from a data distribution, and “symbolic” or algorithmic, where an artist designs the primary parameters and an autonomous system generates samples within these constraints. In this work, we propose a new hybrid genre: neuro-symbolic generative art. As a preliminary study, we train a generative deep neural network on samples from the symbolic approach. We demonstrate through human studies that subjects find the final artifacts and the creation process using our neuro-symbolic approach to be more creative than the symbolic approach 61% and 82% of the time respectively.

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AUTHORS

Written by

Devi Parikh

Gunjan Aggarwal

Publisher

International Conference on Computational Creativity (ICCC)

Research Topics

Computer Vision

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