Portugal

ICCC&#39;20

Performance

technical paper

Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviours that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalising what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.


ICCC'20

The ICCC conference series organized by the Association for Computational Creativity since 2010 is the only scientific conference that focuses on computational creativity alone and also covers all aspects of it.
Computational creativity is the art, science, philosophy and engineering of computational systems which, by taking on particular responsibilities, exhibit behaviors that unbiased observers would deem to be creative. As a field of research, this area is thriving, with progress in formalizing what it means for software to be creative, along with many exciting and valuable applications of creative software in the sciences, the arts, literature, gaming and elsewhere.



"Neural style transfer is a popular deep learning algorithm to generate images to mimic human artistry. This work applies the psychological method of the two-alternative forced choice (2afc) task to measure aesthetic preferences for neural style generated images.
Portrait photos of three popular celebrities were generated by varying three parameters of neural style transfer in five configuration levels. Participants had to choose the image they preferred aesthetically from all pairwise combinations of con-figurations per style. The rate of being chosen was calculated for each neural style transfer configuration level. The findings show a differentiated picture of aesthetic preferences. On the one side, they indicate that people prefer images rendered with 500 iterations and a learning rate of 2e1, i.e. configurations that allow them to recognize the structure of the portrait image despite the stylization. On the other side, aesthetic preferences peak for two distinctly differ-ent content-to-style weight ratios. Whereas the medium-high configuration (100:100) may be favored by people who like abstract arts, the high configuration (300:100) may be chosen by people who prefer realistic art.
These results indicate that aesthetic preferences for neural style transfer-generated images can be characterized by unique pat-terns, and their optimal configuration levels can be captured by the 2afc task. "

Uncovering Aesthetic Preferences of Neural Style Transfer-Generated Images with the Two-Alternative-Forced-Choice Task

Music and Poetry 1

Philosophy and Evaluation 1

Visual Creativity 2

Philosophy and Evaluation 2

Music and Poetry 2

Design and CC

Keynote: Simon Lucas

Language and Narrative 2

Co-Creativity and Support 2

https://d3smihljt9218e.cloudfront.net/transcript/1890/subtitle/1297342fad3b860bd0fc431d79f09661.vtt

English (automatic)

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Uncovering Aesthetic Preferences of Neural Style Transfer-Generated Images with the Two-Alternative-Forced-Choice Task

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Uncovering Aesthetic Preferences of Neural Style Transfer-Generated Images with the Two-Alternative-Forced-Choice Task

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