Creating Variation When Building Image Generation Datasets
Jhovani Gallardo Moreno, Omar Khan, and Michael Wehar

Proceedings of Bridges 2025: Mathematics and the Arts
Pages 507–510
Short Papers

Abstract

We investigate how an algorithmic artist can parameterize drawing algorithms to create a variety of resulting images. We explore how this variation leads to an image dataset that defines a visual style. Furthermore, having a dataset of hundreds of distinctive images rather than a single output image adds more value and enables a variety of applications.

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