ScholarGate
Msaidizi
Machine learningDeep learning / NLP / CV

Modeli ya uenezaji inayojumuisha kikoa (Domain-Adaptive Diffusion Model)

Modeli ya uenezaji inayojumuisha kikoa ni modeli ya uenezaji ya kutenganisha kelele (denoising diffusion probabilistic model - DDPM) ambayo hufunzwa awali kwa seti kubwa za data za jumla kisha hurekebishwa — kupitia urekebishaji mdogo (fine-tuning), ubadilishaji wa maandishi (textual inversion), au LoRA — ili kutoa matokeo ya ubora wa juu katika kikoa maalum kinacholengwa. Inachanganya uwezo mkuu wa kutoa wa modeli za uenezaji na mbinu za kurekebisha kikoa, kuwezesha utengenezaji wa uhalisia wa hali ya juu katika maeneo maalumu kama vile upigaji picha wa kimatibabu, upigaji picha wa setilaiti, au mitindo maalum ya sanaa kwa data kidogo ya kikoa kinacholengwa.

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Vyanzo

  1. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems, 33, 6840–6851. link
  2. 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

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Domain-Adaptive Diffusion Model. ScholarGate. https://scholargate.app/sw/deep-learning/domain-adaptive-diffusion-model

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Imerejelewa na

ScholarGateDomain-adaptive diffusion model (Domain-Adaptive Diffusion Model). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/domain-adaptive-diffusion-model · Seti ya data: https://doi.org/10.5281/zenodo.20539026