An ontological assessment proposal for architectural outputs of generative adversarial network
Can Uzun,
Raşit Eren Cangür
Abstract:Purpose
This study presents an ontological approach to assess the architectural outputs of generative adversarial networks. This paper aims to assess the performance of the generative adversarial network in representing building knowledge.
Design/methodology/approach
The proposed ontological assessment consists of five steps. These are, respectively, creating an architectural data set, developing ontology for the architectural data set, training the You Only Look Once object detection with labels within the … Show more
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