Explaining CLIP through Co-Creative Drawings and Interaction

• URL: http://arxiv.org/abs/2306.07429v1
• Date: Mon, 12 Jun 2023 21:15:25 GMT
• Title: Explaining CLIP through Co-Creative Drawings and Interaction
• Authors: Varvara Guljajeva and Mar Canet Sol\`a and Isaac Joseph Clarke
• Abstract summary: This paper analyses a visual archive of drawings produced by an interactive robotic art installation where audience members narrated their dreams into a system powered by CLIPdraw deep learning (DL) model. The resulting archive of prompt-image pairs were examined and clustered based on concept representation accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper analyses a visual archive of drawings produced by an interactive robotic art installation where audience members narrated their dreams into a system powered by CLIPdraw deep learning (DL) model that interpreted and transformed their dreams into images. The resulting archive of prompt-image pairs were examined and clustered based on concept representation accuracy. As a result of the analysis, the paper proposes four groupings for describing and explaining CLIP-generated results: clear concept, text-to-text as image, indeterminacy and confusion, and lost in translation. This article offers a glimpse into a collection of dreams interpreted, mediated and given form by Artificial Intelligence (AI), showcasing oftentimes unexpected, visually compelling or, indeed, the dream-like output of the system, with the emphasis on processes and results of translations between languages, sign-systems and various modules of the installation. In the end, the paper argues that proposed clusters support better understanding of the neural model.

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