Quantifying combanitorial capabilities of image-generating AI
This study explores the combinatorial capabilities of Image-Generating AI technologies, specifically assessing the ability of these models to generate images based on the combination of multiple input objects. The objects used in the prompts were categorized based on their frequency of appearance...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
Nanyang Technological University
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/175738 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | This study explores the combinatorial capabilities of Image-Generating AI technologies,
specifically assessing the ability of these models to generate images based on the
combination of multiple input objects. The objects used in the prompts were categorized
based on their frequency of appearance in the training datasets, leading to the creation of
various combination types. The primary aim was to determine which combination type leads
to the most accurate image generation. To achieve this, language models were employed to
construct prompts with selected objects, and the resulting images were generated and
compared against actual photographs. The resemblance was quantified using the Fréchet
Inception Distance (FID) scores. |
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