We ask what it means to understand a concept in a visual format by integrating knowledge bases into image generation. We source our knowledge from semantic word embeddings that hold the meanings and understanding of words people use in everyday language, and incorporate them into a class of machine learning frameworks known as Generative Adversarial Networks (GANs). We aim to generate visually indeterminate images for concepts that are obscure to imagine, and question the creativity of the machine in doing so.
Art and Art History; Mathematics
Issak, Alayt Abraham, "Visualizing Concepts: Generative Adversarial Network (GAN) visuals synthesized from semantic vectors" (2021). Senior Independent Study Theses. Paper 9520.
Artificial Intelligence and Robotics | Data Science | Interdisciplinary Arts and Media | Other Mathematics | Probability
Cognitive Computational Creativity, Generative Adversarial Networks (GAN), Deep Learning, Artificial Intelligence Art
Bachelor of Arts
Senior Independent Study Thesis
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