| id |
acadia22_672 |
| authors |
Johanes, Mikhael; Huang, Jeffrey |
| year |
2022 |
| title |
Latent Isovist |
| source |
ACADIA 2022: Hybrids and Haecceities [Proceedings of the 42nd Annual Conference of the Association of Computer Aided Design in Architecture (ACADIA) ISBN 979-8-9860805-8-1]. University of Pennsylvania Stuart Weitzman School of Design. 27-29 October 2022. edited by M. Akbarzadeh, D. Aviv, H. Jamelle, and R. Stuart-Smith. 672-683. |
| summary |
This research leverages the development in deep learning research to develop an experimental framework for discovering machine-human interpretable spatial properties from latent isovist, a reduced dimensionality isovist representation obtained from generative adversarial networks (GANs). GAN latent space contains a wide range of semantically interpretable directions, potentially being used to quantify the spatial properties encoded in isovist representation. |
| series |
ACADIA |
| type |
paper |
| email |
|
| full text |
 file.pdf (8,514,748 bytes) |
| references |
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| last changed |
2024/02/06 14:04 |
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