About: Human-in-the-Loop Design with Machine Learning

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type	http://eprints.org/ontology/ConferenceItemEPrint http://eprints.org/ontology/EPrint Article Academic Article
seeAlso	HTML Summary of #78092 Human-in-the-Loop Design with Machine Learning
sameAs	Human-in-the-Loop Design with Machine Learning
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http://eprints.org/ontology/hasPublished	Human-in-the-Loop Design with Machine Learning (PDF)
dc:hasVersion	Human-in-the-Loop Design with Machine Learning (PDF) Human-in-the-Loop Design with Machine Learning (PDF)
Title	Human-in-the-Loop Design with Machine Learning
described by	https://demo.openlinksw.com/about/id/entity/https/www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://demo.openlinksw.com/about/id/entity/https/raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
Date	2019-07-26
Creator	Chao Wu Danlin Peng Pan Wang Yike Guo Peter Childs Liuqing Chen Xiaoyi Wang L. Li
status	peer reviewed published
Publisher	Cambridge University Press
abstract	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.
Is Part Of	Proceedings of the Design Society: International Conference on Engineering Design https://kar.kent.ac.uk/id/repository
Subject	BF Psychology BF41 Psychology and philosophy NX Arts in general Q Science Q335 Artificial intelligence T Technology
list of authors	https://kar.kent.ac.uk/id/eprint/78092#authors
presented at	ICED 2019
issue	1
volume	1
is topic of	https://www.cs.kent.ac.uk/people/staff/akj22/materials/CO644/export_kar_RDFN3.n3 https://raw.githubusercontent.com/annajordanous/CO644Files/main/export_kar_RDFN3.n3
is primary topic of	HTML Summary of #78092 Human-in-the-Loop Design with Machine Learning

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