The Digital Synaptic Neural Substrate: Size and Quality Matters

TL;DR

This paper explores how the size and quality of seed images affect the performance of the Digital Synaptic Neural Substrate (DSNS) computational creativity method, demonstrating that larger and clearer images lead to higher quality outputs.

Contribution

It provides experimental evidence that larger and higher-quality images improve DSNS-generated creative outputs, extending previous work on image-based seed inputs.

Findings

Larger seed images enhance output quality.

Clearer images produce better results.

Size and quality of images significantly influence DSNS performance.

Abstract

We investigate the 'Digital Synaptic Neural Substrate' (DSNS) computational creativity approach further with respect to the size and quality of images that can be used to seed the process. In previous work we demonstrated how combining photographs of people and sequences taken from chess games between weak players can be used to generate chess problems or puzzles of higher aesthetic quality, on average, compared to alternative approaches. In this work we show experimentally that using larger images as opposed to smaller ones improves the output quality even further. The same is also true for using clearer or less corrupted images. The reasons why these things influence the DSNS process is presently not well-understood and debatable but the findings are nevertheless immediately applicable for obtaining better results.