Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muundo wa Kuenea kwa Lugha Nyingi× | Mfumo Ulioboreshwa wa Kuenea× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili | 2020–2023 | 2020–2023 |
| Mwanzilishi≠ | Ho, J., Jain, A., & Abbeel, P. (diffusion foundation); multilingual NLP extensions by various authors (2022–2024) | Ho, J., Jain, A., Abbeel, P. (base DDPM); Ruiz et al. (DreamBooth fine-tuning paradigm) |
| Aina≠ | Generative model (denoising diffusion process, multilingual extension) | Generative model (fine-tuned via subject-specific or domain-specific data) |
| Chanzo asilia≠ | 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. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 22500–22510. DOI ↗ |
| Majina mbadala | Multilingual DiffuSeq, Cross-lingual Diffusion Model, Multilingual DDPM, Multilingual Denoising Diffusion | DDPM fine-tuning, diffusion model adaptation, personalized diffusion model, subject-driven diffusion fine-tuning |
| Zinazohusiana | 5 | 5 |
| Muhtasari≠ | A Multilingual Diffusion Model adapts the denoising diffusion probabilistic framework to work across multiple languages, enabling cross-lingual text generation, translation, and language-agnostic content synthesis. By conditioning on multilingual representations, the diffusion process learns a shared latent space that spans linguistic boundaries, producing high-quality outputs for low- and high-resource languages alike. | A fine-tuned diffusion model adapts a large pretrained denoising diffusion model — such as Stable Diffusion or DALL-E — to a specific subject, style, or domain by continuing training on a small curated dataset. Techniques such as DreamBooth, textual inversion, and LoRA make this adaptation feasible on consumer hardware while preserving general generative capability. |
| ScholarGateSeti ya data ↗ |
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