ScholarGate
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Machine learning

Variational Autoencoder (VAE)

Autoencoder wa kawaida husukuma kila pembejeo kwenye nukta moja katika nafasi iliyobanwa, ambayo ni nzuri kwa ujenzi lakini haitoi njia ya msingi ya kuunda data mpya. VAE badala yake huweka kila pembejeo kwenye wingu dogo la uwezekano — wastani na usambazaji — na hulazimisha mawingu hayo kukaa karibu na usambazaji rahisi wa marejeleo. Kwa sababu nafasi ya siri ni laini na inayoendelea, unaweza kuchora nukta mpya kutoka kwayo na kirekebishaji huibadilisha kuwa mfano mpya kabisa, unaowezekana.

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Vyanzo

  1. Kingma, D. P. & Welling, M. (2014). Auto-Encoding Variational Bayes. International Conference on Learning Representations (ICLR). link
  2. Higgins, I. et al. (2017). beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework. International Conference on Learning Representations (ICLR). link

Jinsi ya kunukuu ukurasa huu

ScholarGate. (2026, June 1). Variational Autoencoder (VAE). ScholarGate. https://scholargate.app/sw/deep-learning/variational-autoencoder

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Imerejelewa na

ScholarGateVariational Autoencoder (Variational Autoencoder (VAE)). Imepatikana 2026-06-15 kutoka https://scholargate.app/sw/deep-learning/variational-autoencoder · Seti ya data: https://doi.org/10.5281/zenodo.20539026