Generative Adversarial Networks Quiz

Answers and explanations appear as you take the quiz.

1. What are the two competing networks in a GAN?
   • An encoder and a decoder
   • A reader and a writer
   • Two identical copies that always agree
   • A generator that makes fakes and a discriminator that judges them
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. What is 'mode collapse' in a GAN?
   • The discriminator deletes all the data
   • The network runs out of memory
   • The generator produces only a few repetitive outputs instead of variety
   • The image resolution doubles automatically
5. Which researchers introduced the GAN framework in the 2014 paper?
   • Alan Turing alone in 1950
   • Ian Goodfellow and colleagues
   • 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