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Human-in-the-Loop Design with Machine Learning

Wang, Pan, Peng, Danlin, Li, Ling, Chen, Liuqing, Wu, Chao, Wang, Xiaoyi, Childs, Peter, Guo, Yike (2019) Human-in-the-Loop Design with Machine Learning. In: Proceedings of the Design Society: International Conference on Engineering Design. 1 (1). pp. 2577-2586. Cambridge University Press (doi:10.1017/dsi.2019.264) (KAR id:78092)

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Deep learning methods have been applied to randomly generate images, such as in fashion, furniture design. To date, consideration of human aspects which play a vital role in a design process has not been given significant attention in deep learning approaches. In this paper, results are reported from a human- in-the-loop design method where brain EEG signals are used to capture preferable design features. In the framework developed, an encoder extracting EEG features from raw signals recorded from subjects when viewing images from ImageNet are learned. Secondly, a GAN model is trained conditioned on the encoded EEG features to generate design images. Thirdly, the trained model is used to generate design images from a person's EEG measured brain activity in the cognitive process of thinking about a design. To verify the proposed method, a case study is presented following the proposed approach. The results indicate that the method can generate preferred designs styles guided by the preference related brain signals. In addition, this method could also help improve communication between designers and clients where clients might not be able to express design requests clearly.

Item Type: Conference or workshop item (Speech)
DOI/Identification number: 10.1017/dsi.2019.264
Additional information: The Distinguished Paper Award, Reviewers' Choice
Uncontrolled keywords: Machine learning, Artificial intelligence, Design cognition, Computational design methods
Subjects: B Philosophy. Psychology. Religion > BF Psychology
B Philosophy. Psychology. Religion > BF Psychology > BF41 Psychology and philosophy
N Visual Arts > NX Arts in general
Q Science
Q Science > Q Science (General) > Q335 Artificial intelligence
T Technology
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Caroline Li
Date Deposited: 31 Oct 2019 00:45 UTC
Last Modified: 16 Feb 2021 14:09 UTC
Resource URI: (The current URI for this page, for reference purposes)
Li, Ling:
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