Machine learning based co-creative design framework

Add code
Jan 23, 2020
Brian Quanz, Wei Sun, Ajay Deshpande, Dhruv Shah, Jae-eun Park
Figure 1 for Machine learning based co-creative design framework
Figure 2 for Machine learning based co-creative design framework
Figure 3 for Machine learning based co-creative design framework
Figure 4 for Machine learning based co-creative design framework

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon

We propose a flexible, co-creative framework bringing together multiple machine learning techniques to assist human users to efficiently produce effective creative designs. We demonstrate its potential with a perfume bottle design case study, including human evaluation and quantitative and qualitative analyses.

* Thirty-third Conference on Neural Information Processing Systems (NeurIPS) 2019 Workshop on Machine Learning for Creativity and Design, December 14th, 2019, Vancouver, Canada (https://neurips2019creativity.github.io/)  
View paper onarxiv icon

Share this with someone who'll enjoy it:

Twitter IconFacebook IconLinkedin IconWhatsapp IconMessenger Icon