Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing

Kazjon Grace; Mary Lou Maher; Nicholas Davis; Pegah Karimi

arxiv: 1801.00723 · v1 · pith:AUCY43KHnew · submitted 2018-01-02 · 💻 cs.LG · cs.AI· stat.ML

Deep Learning for Identifying Potential Conceptual Shifts for Co-creative Drawing

Pegah Karimi , Nicholas Davis , Kazjon Grace , Mary Lou Maher This is my paper

classification 💻 cs.LG cs.AIstat.ML

keywords systemcategoriesco-creativedrawingsketchesconceptualidentifyingshifts

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We present a system for identifying conceptual shifts between visual categories, which will form the basis for a co-creative drawing system to help users draw more creative sketches. The system recognizes human sketches and matches them to structurally similar sketches from categories to which they do not belong. This would allow a co-creative drawing system to produce an ambiguous sketch that blends features from both categories.

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Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System
cs.HC 2019-06 unverdicted novelty 4.0

Deep learning vector novelty metric drives conceptual shifts in an AI-human sketching system; user study finds higher novelty correlates with more creative design outcomes.