Quantifying combinatorial capababilities of image-generating AI
This study investigates the combinatorial capabilities of state-of-the-art generative AI models—Midjourney, DALL-E, and Stable Diffusion—when tasked with generating images containing underrepresented object combinations. Utilizing the LAION400M dataset as a proxy for the models' training data...
Saved in:
Main Author: | Poon, Wei Kang |
---|---|
Other Authors: | Li Boyang |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2024
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/181225 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Quantifying combanitorial capabilities of image-generating AI
by: Karanam, Akshit
Published: (2024) -
Generative AI-based ChatGPT and AI chatbots for learning AI education
by: Chin, Woon Ping
Published: (2024) -
Development of perceptual image quality database and assessment for AI generated facial images
by: Tan, Xin Kai
Published: (2024) -
Generative AI and education
by: Chieng, Shannon Shuen Ern
Published: (2024) -
Generative AI art - hypernetworks
by: Chee, Mei Qi
Published: (2024)