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Machine learningDeep learning / NLP / CV

Semi-supervised Variational Autoencoder (M1/M2 Generative Model)

Uwekaji lebo mwingi ni ghali, kwa hivyo hifadhi za data mara nyingi huwa na mifano mingi zaidi isiyo na lebo kuliko iliyo na lebo. Kiainishaji cha kawaida hutupa data isiyo na lebo. VAE yenye usimamizi nusu badala yake huchukulia lebo ya nukta isiyo na lebo kama kigezo kingine fiche cha kugharamia, na hufunza mwanamitindo wa kuzalisha (dekoda) na mtandao wa kutambua (enkoda pamoja na kiainishaji) pamoja. Kwa sababu mwanamitindo lazima urejeshe pembejeo kila moja iwe na lebo au la, mifano isiyo na lebo huathiri kikamilifu uwakilishi uliojifunzwa, ikiboresha uainishaji kwa viwango vya chini sana vya lebo.

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Vyanzo

  1. Kingma, D. P., Mohamed, S., Rezende, D. J., & Wierstra, D. (2014). Semi-supervised learning with deep generative models. Advances in Neural Information Processing Systems (NeurIPS), 27, 3581–3589. link
  2. Kingma, D. P., & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR 2014). link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 3). Semi-supervised Variational Autoencoder (M1/M2 Generative Model). ScholarGate. https://scholargate.app/sw/deep-learning/semi-supervised-variational-autoencoder

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ScholarGateSemi-supervised Variational Autoencoder (Semi-supervised Variational Autoencoder (M1/M2 Generative Model)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/semi-supervised-variational-autoencoder · Seti ya data: https://doi.org/10.5281/zenodo.20539026