Abstract
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. Since IEC is good at optimizing system parameters on the basis of human subjective criteria, the user can obtain preferable lifelike design image. Experiment results show the high performance of this method.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Goodfellow, I.: NIPS 2016 tutorial: generative adversarial networks. arXiv preprint arXiv:1701.00160 (2016)
Takagi, H.: Interactive evolutionary computation: fusion of the capabilities of EC optimization and human evaluation. Proc. IEEE 89(9), 1275–1296 (2001)
Arakawa, K., Nomoto, K.: A system for beautifying face images using interactive evolutionary computing. In: Proceedings of the 2005 International Symposium on Intelligent Signal Processing and Communication Systems, pp. 9–12, December 2005
Bontrager, P., Lin, W., Togelius, J., Risi, S.: Deep interactive evolution. In: Liapis, A., Romero Cardalda, J.J., Ekárt, A. (eds.) EvoMUSART 2018. LNCS, vol. 10783, pp. 267–282. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77583-8_18
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: ICLR (2016)
Zhu, J., et al.: Toward multimodal image-to-image translation. In: Advances in Neural Information Processing Systems (2017)
Isola, P., Zhu, J., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017)
Sumi, S., Oinuma, J., Arakawa, K.: Interactive evolutionary image processing for face beautification using smaller population size. In: Proceedings of the ISPACS 2012, pp. 48–53, November 2012
Yu, A., Grauman, K.: Fine-grained visual comparisons with local learning. In: Computer Vision and Pattern Recognition (CVPR), June 2014
Zhu, J.: https://github.com/junyanz/iGAN/blob/master/train_dcgan/README.md. Accessed Jan 2021
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Xin, C., Arakawa, K. (2021). Object Design System by Interactive Evolutionary Computation Using GAN with Contour Images. In: Zimmermann, A., Howlett, R.J., Jain, L.C., Schmidt, R. (eds) Human Centred Intelligent Systems . KES-HCIS 2021. Smart Innovation, Systems and Technologies, vol 244. Springer, Singapore. https://doi.org/10.1007/978-981-16-3264-8_7
Download citation
DOI: https://doi.org/10.1007/978-981-16-3264-8_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3263-1
Online ISBN: 978-981-16-3264-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)