A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images

• URL: http://arxiv.org/abs/2502.21151v2
• Date: Mon, 10 Mar 2025 22:22:19 GMT
• Title: A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images
• Authors: Zineb Sordo, Eric Chagnon, Daniela Ushizima,
• Abstract summary: This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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