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DC Field | Value | Language |
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dc.contributor.author | Zammit, Marvin | - |
dc.contributor.author | Liapis, Antonios | - |
dc.contributor.author | Yannakakis, Georgios N. | - |
dc.date.accessioned | 2022-10-06T08:58:19Z | - |
dc.date.available | 2022-10-06T08:58:19Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Zammit, M., Liapis, A. & Yannakakis, G. N. (2022). Seeding diversity into AI art paper type : technical paper. 13th International Conference on Computational Creativity, Bolzano. | en_GB |
dc.identifier.uri | https://www.um.edu.mt/library/oar/handle/123456789/102367 | - |
dc.description.abstract | This paper argues that generative art driven by conformance to a visual and/or semantic corpus lacks the necessary criteria to be considered creative. Among several issues identified in the literature, we focus on the fact that generative adversarial networks (GANs) that create a single image, in a vacuum, lack a concept of novelty regarding how their product differs from previously created ones. We envision that an algorithm that combines the novelty preservation mechanisms in evolutionary algorithms with the power of GANs can deliberately guide its creative process towards output that is both good and novel. In this paper, we use recent advances in image generation based on semantic prompts using OpenAI’s CLIP model, interrupting the GAN’s iterative process with short cycles of evolutionary divergent search. The results of evolution are then used to continue the GAN’s iterative process; we hypothesise that this intervention will lead to more novel outputs. Testing our hypothesis using novelty search with local competition, a quality-diversity evolutionary algorithm that can increase visual diversity while maintaining quality in the form of adherence to the semantic prompt, we explore how different notions of visual diversity can affect both the process and the product of the algorithm. Results show that even a simplistic measure of visual diversity can help counter a drift towards similar images caused by the GAN. This first experiment opens a new direction for introducing higher intentionality and a more nuanced drive for GANs. | en_GB |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computational Creativity | en_GB |
dc.rights | info:eu-repo/semantics/openAccess | en_GB |
dc.subject | Art | en_GB |
dc.subject | Artificial intelligence | en_GB |
dc.subject | Computer art | en_GB |
dc.title | Seeding diversity into AI art paper type : technical paper | en_GB |
dc.type | conferenceObject | en_GB |
dc.rights.holder | The copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder. | en_GB |
dc.bibliographicCitation.conferencename | 13th International Conference on Computational Creativity | en_GB |
dc.bibliographicCitation.conferenceplace | Bolzano, Italy. 27/06-01/07/2022. | en_GB |
dc.description.reviewed | peer-reviewed | en_GB |
Appears in Collections: | Scholarly Works - InsDG |
Files in This Item:
File | Description | Size | Format | |
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Seeding_diversity_into_AI_art_paper_type_technical_paper_2022.pdf | 2.9 MB | Adobe PDF | View/Open |
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