Idanwo ti o sopọ mọ itọsọna · Alabọde Ipele

Generative Adversarial Networks adanwo

Review generator and discriminator roles, original GAN inputs, adversarial feedback, mode collapse, historical origins, equilibrium, and conditioning.

Jẹmọ itọsọna awọn ọnaAwọn nẹtiwọọki Ọta Generative
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Kini awọn nẹtiwọọki idije meji ni GAN kan?

Awotẹlẹ gbogbo ibeere8 ibeere

Awọn idahun ati awọn alaye han bi o ṣe n mu ibeere naa.

  1. Kini awọn nẹtiwọọki idije meji ni GAN kan?
    • Ohun kooduopo ati ki o kan decoder
    • Oluka ati onkqwe
    • Awọn ẹda kanna meji ti o gba nigbagbogbo
    • A monomono ti o ṣe fakes ati ki o kan discriminator ti o ṣe idajọ wọn
  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. Kini 'ipo iṣubu' ni GAN kan?
    • Awọn discriminator pa gbogbo awọn data
    • Awọn nẹtiwọki nṣiṣẹ jade ti iranti
    • Olupilẹṣẹ ṣe agbejade awọn abajade atunwi diẹ nikan dipo oriṣiriṣi
    • Ipinnu aworan naa ni ilọpo meji laifọwọyi
  5. Which researchers introduced the GAN framework in the 2014 paper?
    • Alan Turing alone in 1950
    • Ian Goodfellow ati awọn ẹlẹgbẹ
    • 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