ScholarGate
Msaidizi
Machine learningDeep learning / NLP / CV

Mabadilishaji ya Macho Yaliyosaidiwa kwa Nusu

Semi-supervised Vision Transformer hutumia usanifu wa kujitegemea wa ViT unaotegemea kiraka katika mazingira ambapo sehemu ndogo tu ya picha zina lebo, ikitumia makusanyo makubwa yasiyo na lebo kupitia uwekaji lebo bandia, udhibiti wa uthabiti, au kazi za awali za kujifunza kwa kujitegemea kabla ya kurekebisha kwenye seti ndogo yenye lebo. Mbinu hii hufikia karibu usahihi wa usimamizi hata wakati picha zenye lebo ni chache.

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Vyanzo

  1. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR 2021). link
  2. Zhai, X., Kolesnikov, A., Houlsby, N., & Beyer, L. (2022). Scaling Vision Transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12104–12113. link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Semi-supervised Vision Transformer (Semi-supervised ViT). ScholarGate. https://scholargate.app/sw/deep-learning/semi-supervised-vision-transformer

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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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ScholarGateSemi-supervised Vision Transformer (Semi-supervised Vision Transformer (Semi-supervised ViT)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/semi-supervised-vision-transformer · Seti ya data: https://doi.org/10.5281/zenodo.20539026