%0 Conference Paper %1 26369ba6dd6b442ebeae14d5f501bb7a %A Ferguson, Mark %A Deterding, Christoph Sebastian %A Lieberoth, Andreas %A Malmdorf Andersen, Marc %A Devlin, Sam %A Kudenko, Daniel %A Walker, James Alfred %B ICCC'20: Eleventh International Conference on Computational Creativity %D 2020 %I Association for Computational Creativity (ACC) %K myown %T Automatic Similarity Detection in LEGO Ducks %X 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.

@inproceedings{26369ba6dd6b442ebeae14d5f501bb7a, 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 {\textquoteleft}born digital{\textquoteright}, 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.}, added-at = {2021-03-04T19:01:37.000+0100}, author = {Ferguson, Mark and Deterding, {Christoph Sebastian} and Lieberoth, Andreas and {Malmdorf Andersen}, Marc and Devlin, Sam and Kudenko, Daniel and Walker, {James Alfred}}, biburl = {https://www.bibsonomy.org/bibtex/288bd318ce96c6f87732aff7cf5489d61/kudenko}, booktitle = {ICCC'20: Eleventh International Conference on Computational Creativity}, day = 30, interhash = {5c087bce9b8809a51e11b4fdf4a42d54}, intrahash = {88bd318ce96c6f87732aff7cf5489d61}, keywords = {myown}, language = {English}, month = jun, publisher = {Association for Computational Creativity (ACC)}, timestamp = {2021-04-16T14:36:17.000+0200}, title = {Automatic Similarity Detection in LEGO Ducks}, year = 2020 }