Kedis ku xidhan hagaha · Dhexdhexaad Heerka

Kediska Shabakadda Adversarial Generative

Dib u eegis doorarka curiyaha iyo midabtakoor, wax gelinta GAN asalka ah, jawaab celinta iska soo horjeeda, qaabka burburka, asalka taariikhiga ah, dheelitirnaanta, iyo qaboojinta.

Wadooyinka haga ee la xidhiidhaShabakadaha Adversarial Generative
Su'aal 1 ee 8

Waa maxay labada shabakadood ee ku tartamaya GAN?

Horudhac su'aal kasta8 su'aalo

Jawaabaha iyo sharraxaadaha waxay soo baxayaan markaad qaadanayso kediska.

  1. Waa maxay labada shabakadood ee ku tartamaya GAN?
    • Cod-bixiye iyo cod-bixiye
    • Akhriste iyo qoraa
    • Laba nuqul oo isku mid ah oo had iyo jeer ku heshiiya
    • Koronto dhaliyaha been abuurka sameeya iyo mid wax kala sooca oo iyaga xukuma
  2. In the original unconditional GAN formulation, what input does the generator transform into a sample?
    • A guaranteed copy of a finished real image
    • Sampled latent noise
    • The discriminator's parameter file
    • A fixed human-written answer key
  3. What adversarial learning signal can reach a GAN generator through its discriminator?
    • Gradients of the generator objective with respect to its parameters
    • A rule that every generated image is correct
    • A prohibition against all conditional image inputs
    • A copy of every discriminator weight as the output image
  4. Waa maxay 'hab burburka' ee GAN?
    • Takoorku wuxuu tirtiraa dhammaan xogta
    • Shabakadu waxay ka dhammaanaysaa xusuusta
    • Dab-dhaliye waxa uu soo saaraa wax soo saar dhawr ah oo soo noqnoqda halkii uu ka lahaa noocyo kala duwan
    • Xallinta sawirku si toos ah ayay u labanlaabantaa
  5. Which researchers introduced the GAN framework in the 2014 paper?
    • Alan Turing alone in 1950
    • Ian Goodfellow iyo asxaabtiisa
    • Geoffrey Hinton alone in 2006
    • Yann LeCun alone in 1998
  6. How can a conventional feed-forward GAN generator produce one sample after training?
    • By requiring exactly a thousand discriminator updates at inference
    • By retrieving an unchanged target image in every case
    • Without evaluating any learned computation
    • With one forward evaluation of the generator
  7. At the ideal original GAN equilibrium where generated and real distributions match, what probability does the optimal discriminator assign to an example being real?
    • Zero for every example
    • One for every example
    • One-half
    • A probability greater than one
  8. In the conditional GAN formulation, where can a class label or other condition be supplied?
    • To both the generator and the discriminator
    • Only to an unrelated output filename
    • To neither network by definition
    • Only after all training and generation have finished