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

Abstract

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.

This is an extended version of the paper presented at 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA. Workshop on Machine Learning for Creativity and Design

References in corpus (2)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Neural Representation of Sketch Drawings

Cited by in corpus (1)

• Deep Learning in a Computational Model for Conceptual Shifts in a Co-Creative Design System