Machine learningDeep learning / NLP / CV
扩散模型迁移学习
使用扩散模型的迁移学习通过在较小的特定领域数据集上继续训练，将大型预训练扩散模型（如 Stable Diffusion 或 DALL-E 2）适配到新的目标领域或任务。实践者无需从头开始学习完整的生成过程，而是利用数百万次训练步骤中已编码的知识，以适度的数 据和计算量实现高质量的领域适配生成。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems (NeurIPS), 33, 6840–6851. link ↗
- Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023). DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation. CVPR 2023. link ↗
如何引用本页
ScholarGate. (2026, June 3). Transfer Learning Applied to Diffusion-Based Generative Models. ScholarGate. https://scholargate.app/zh/deep-learning/transfer-learning-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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