Arakawa Kaoru    Department   Undergraduate School  , School of Interdisciplinary Mathematical Sciences    Position   Professor
Language	English
Publication Date	2021/05
Type	International conference proceedings
Peer Review	Peer reviewed
Title	Object Design System by Interactive Evolutionary Computation Using GAN with Contour Images
Contribution Type	Co-authored (other than first author)
Journal	Human Centered Intelligent Systems
Journal Type	Another Country
Publisher	Springer Nature
Volume, Issue, Page	pp.66-75
Total page number	242
Authorship	Corresponding author
Author and coauthor	Chen Xin, Kaoru Arakawa
Details	This paper proposes a method to design objects by interactive evolutionary computation (IEC) using generative adversarial network (GAN) with the contour image of the object which the user wants to design. Here, a conditional GAN is adopted in order to utilize the contour image. GANs can generate lifelike images of objects, however, it is hard to control the output, since GAN just generates images randomly with the distribution learned from training data. Thus, IEC is introduced here to control the latent vectors in the conditional GAN. Moreover, using the contour image, conditional GAN can efficiently generate images of objects which the user prefers. In the proposed method the latent vectors in the conditional GAN are optimized for each user, so that the design is satisfactory to him/her through the process of IEC. Experiment results show the high performance of this method.