综述生成式AI在图文与图像生成中的进展及对科学图像的意义
A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images
- 对比变分自编码器、对抗网络与扩散模型三类主流架构
- 分析各类方法在科学图像理解中的优劣与适用场景
- 适合关注生成式AI在科研中应用的学者与工程师
本文综述了生成式AI在文本到图像和图像到图像生成领域的最新进展。对三种主流架构——变分自编码器（VAE）、生成对抗网络（GAN）和扩散模型（Diffusion Models）进行了比较分析，阐明其核心原理、架构创新以及在科学图像理解中的实际优势与局限性。最后讨论了该领域当前的关键挑战与未来研究方向，为相关研究提供参考。
原文摘要 · Abstract (English)
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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