| id |
ecaade2022_78 |
| authors |
Eroglu, Ruşen and Gül, Leman Figen |
| year |
2022 |
| title |
Architectural Form Explorations through Generative Adversarial Networks - Predicting the potentials of StyleGAN |
| source |
Pak, B, Wurzer, G and Stouffs, R (eds.), Co-creating the Future: Inclusion in and through Design - Proceedings of the 40th Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2022) - Volume 2, Ghent, 13-16 September 2022, pp. 575582 |
| doi |
https://doi.org/10.52842/conf.ecaade.2022.2.575
|
| summary |
In recent years, generative models have been rapidly transforming into a broad field of research, and artificial intelligence (AI) works are increasing. Since deep learning technologies such as Generative Adversarial Networks (GANs) providing synthesized new images are becoming more accessible, researchers in the design and related fields are very much interested in adapting GANs into practice. Especially, StyleGAN has a strong capability for image learning, reconstruction simulation, and absorbing the pixel characteristics of images in the input dataset. StyleGAN also produces similar imitation outputs and summarizes all the input data into one "average output". The study aims to reveal the potential of these outputs that can be employed as a visual inspiration aid for designers. This article will discuss the outputs of the experiments, findings, and prospects of StyleGAN. |
| keywords |
Artificial Intelligence, Machine Learning, Generative Adversarial Networks, StyleGAN |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,446,602 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/04/22 07:10 |
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