Machine learningDeep learning / NLP / CV
域自适应扩散模型
域自适应扩散模型是一种去噪扩散概率模型（DDPM），它在大型通用数据集上进行预训练，然后通过微调（fine-tuning）、文本反转（textual inversion）或 LoRA 等方法进行适应，以在特定的目标域中生成高质量的输出。它结合了扩散模型的强大生成能力和域适应技术，能够在医学影像、卫星影像或特定领域艺术风格等专业领域实现高保真合成，即使目标域数据有限。
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来源
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗
- Gal, R., Alaluf, Y., Atzmon, Y., Patashnik, O., Bermano, A. H., Chechik, G., & Cohen-Or, D. (2023). An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion. International Conference on Learning Representations (ICLR 2023). link ↗
如何引用本页
ScholarGate. (2026, June 3). Domain-Adaptive Diffusion Model. ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-diffusion-model
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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