Shallow Art: Art Extension Through Simple Machine Learning
Computer Vision and Pattern Recognition
2019-10-25 v1 Machine Learning
Machine Learning
Abstract
Shallow Art presents, implements, and tests the use of simple single-output classification and regression models for the purpose of art generation. Various machine learning algorithms are trained on collections of computer generated images, artworks from Vincent van Gogh, and artworks from Rembrandt van Rijn. These models are then provided half of an image and asked to complete the missing side. The resulting images are displayed, and we explore implications for computational creativity.
Keywords
Cite
@article{arxiv.1910.11118,
title = {Shallow Art: Art Extension Through Simple Machine Learning},
author = {Kyle Robinson and Dan Brown},
journal= {arXiv preprint arXiv:1910.11118},
year = {2019}
}
Comments
5 pages, 9 figures, presented at the 10th International Conference on Computational Creativity (ICCC 2019)