方法对比
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| 域自适应扩散模型× | 多模态扩散模型× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2022–2023 | 2020–2022 |
| 提出者≠ | Ho et al. (DDPM); domain-adaptation variants popularized by Gal et al. and Ruiz et al. (2022–2023) | Ho, J. et al. (DDPM); Rombach, R. et al. (LDM/Stable Diffusion) |
| 类型≠ | Generative model with domain adaptation | Generative model (denoising diffusion) |
| 开创性文献≠ | Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link ↗ | Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 10684–10695. DOI ↗ |
| 别名 | DA-diffusion model, domain-adapted diffusion model, domain-adaptive DDPM, cross-domain diffusion model | multimodal DDPM, cross-modal diffusion, conditional multimodal diffusion, multi-modal denoising diffusion |
| 相关 | 6 | 6 |
| 摘要≠ | A domain-adaptive diffusion model is a denoising diffusion probabilistic model (DDPM) that is pre-trained on large general datasets and then adapted — through fine-tuning, textual inversion, or LoRA — to generate high-quality outputs in a specific target domain. It combines the powerful generative capacity of diffusion models with domain adaptation techniques, enabling high-fidelity synthesis in specialized areas such as medical imaging, satellite imagery, or domain-specific art styles with limited target-domain data. | A multimodal diffusion model extends denoising diffusion probabilistic models to generate or understand content by conditioning on signals from multiple modalities — such as text, image, audio, or video — simultaneously. It learns to reverse a noise process guided by cross-modal context, enabling high-fidelity synthesis and translation across modalities. |
| ScholarGate数据集 ↗ |
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