ChildDiffusion: Unlocking the Potential of Generative AI and Controllable Augmentations for Child Facial Data Using Stable Diffusion and Large Language Models
Ensuring the availability of child facial datasets is essential for advancing AI applications, yet legal, ethical, and data scarcity concerns pose significant challenges. Current generative models such as StyleGAN excel at producing synthetic facial data but struggle with temporal consistency, contr...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11021410/ |
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