Inventing art styles with no artistic training data

• URL: http://arxiv.org/abs/2305.12015v2
• Date: Tue, 19 Dec 2023 00:07:16 GMT
• Title: Inventing art styles with no artistic training data
• Authors: Nilin Abrahamsen, Jiahao Yao
• Abstract summary: We propose two procedures to create painting styles using models trained only on natural images. In the first procedure we use the inductive bias from the artistic medium to achieve creative expression. The second procedure uses an additional natural image as inspiration to create a new style.
• Score: 0.65268245109828
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose two procedures to create painting styles using models trained only on natural images, providing objective proof that the model is not plagiarizing human art styles. In the first procedure we use the inductive bias from the artistic medium to achieve creative expression. Abstraction is achieved by using a reconstruction loss. The second procedure uses an additional natural image as inspiration to create a new style. These two procedures make it possible to invent new painting styles with no artistic training data. We believe that our approach can help pave the way for the ethical employment of generative AI in art, without infringing upon the originality of human creators.

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