Hays, K. MichaelSinha, PawanOsmany, Sabrina2023-06-0220232023-05-152023-05Osmany, Sabrina. 2023. Visualizing Semiotics with Generative Adversarial Networks. Doctoral dissertation, Harvard University Graduate School of Arts and Sciences.30492709https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37375889Designers use semiotics to create visual artifacts. The design for a flight attendant’s uniform may be modified to look more “alert,” less “austere,” or more “practical.” The form of a house may be modified to appear more “futuristic,” a car more “friendly,” and a pair of sneakers, “evil.” This process depends on a designer’s existing visual and conceptual vocabulary. This thesis develops a computational approach to augment a designer’s visual form-finding process and enables the designer to discover visual forms beyond her explicit visual and conceptual repertoire. It shows that visual semiotics can be learned by a neural network which can then be used to explore the latent space of a Generative Adversarial Network (GAN). Simultaneously it shows that exploring the latent space of a GAN using abstract language expands the range of visual possibilitiesapplication/pdfenArtificial IntelligenceDesignGenerative ModelsMachine LearningSemioticsArtificial intelligenceDesignArchitectureThesis or Dissertation2023-06-02https://orcid.org/0009-0004-6763-9770