Automatic Similarity Detection in LEGO Ducks

Mark Ferguson, Christoph Sebastian Deterding, Andreas Lieberoth, Marc Malmdorf Andersen, Sam Devlin, Daniel Kudenko, James Alfred Walker

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationICCC'20: Eleventh International Conference on Computational Creativity
PublisherAssociation for Computational Creativity (ACC)
Number of pages4
Publication statusAccepted/In press - 30 Jun 2020

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