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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2502.21151 |
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| _version_ | 1866929752446599168 |
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| author | Sordo, Zineb Chagnon, Eric Ushizima, Daniela |
| author_facet | Sordo, Zineb Chagnon, Eric Ushizima, Daniela |
| contents | This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_21151 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images Sordo, Zineb Chagnon, Eric Ushizima, Daniela Computer Vision and Pattern Recognition This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field. |
| title | A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2502.21151 |