| id |
ecaadesigradi2019_135 |
| authors |
Newton, David |
| year |
2019 |
| title |
Deep Generative Learning for the Generation and Analysis of Architectural Plans with Small Datasets |
| doi |
https://doi.org/10.52842/conf.ecaade.2019.2.021
|
| source |
Sousa, JP, Xavier, JP and Castro Henriques, G (eds.), Architecture in the Age of the 4th Industrial Revolution - Proceedings of the 37th eCAADe and 23rd SIGraDi Conference - Volume 2, University of Porto, Porto, Portugal, 11-13 September 2019, pp. 21-28 |
| summary |
The field of generative architectural design has explored a wide range of approaches in the automation of design production, but these approaches have demonstrated limited artificial intelligence. Generative Adversarial Networks (GANs) are a leading deep generative model that use deep neural networks (DNNs) to learn from a set of training examples in order to create new design instances with a degree of flexibility and fidelity that outperform competing generative approaches. Their application to generative tasks in architecture, however, has been limited. This research contributes new knowledge on the use of GANs for architectural plan generation and analysis in relation to the work of specific architects. Specifically, GANs are trained to synthesize architectural plans from the work of the architect Le Corbusier and are used to provide analytic insight. Experiments demonstrate the efficacy of different augmentation techniques that architects can use when working with small datasets. |
| keywords |
generative design; deep learning; artificial intelligence; generative adversarial networks |
| series |
eCAADeSIGraDi |
| email |
|
| full text |
 file.pdf (3,842,494 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:58 |
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