Ferguson, Mark, Deterding, Christoph Sebastian orcid.org/0000-0003-0033-2104, Lieberoth, Andreas et al. (4 more authors) (Accepted: 2020) Automatic Similarity Detection in LEGO Ducks. In: ICCC'20: Eleventh International Conference on Computational Creativity. Association for Computational Creativity (ACC) (In Press)
Abstract
The automated evaluation of creative products promises both good-and-scalable creativity assessments and new forms of visual analysis of whole corpora. Where creative works are not ‘born digital’, such automated evaluation requires fast and frugal ways of transforming them into data representations that can be meaningfully assessed with common creativity metrics like novelty. In this paper, we report the results of training a Spatiotemporal DeepInfomax Variational Autoencoder (STDIM-VAE) on a digital photo pool of 162 LEGO ducks to generate a phenotypical landscape of clusters of similar ducks and dissimilarity scores for individual ducks. Visual inspection suggests that our system produces plausible results from image pixels alone. We conclude that under certain conditions, STDIM-VAEs may provide fast and frugal ways of automatically assessing corpora of creative works.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) The University of York > Faculty of Arts and Humanities (York) > Theatre, Film, TV and Interactive Media (York) |
Funding Information: | Funder Grant number EPSRC EP/M023265/1 EPSRC EP/L015846/1 |
Depositing User: | Pure (York) |
Date Deposited: | 14 Jul 2020 14:10 |
Last Modified: | 21 Jan 2025 18:25 |
Status: | In Press |
Publisher: | Association for Computational Creativity (ACC) |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:163211 |
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Filename: ICCC20_paper_146_1_.pdf
Description: Automatic_Similarity_Detection_LEGO_Ducks