생성적 적대 신경망 퀴즈

퀴즈를 풀면 정답과 설명이 나타납니다.

1. GAN에서 두 개의 경쟁 네트워크는 무엇입니까?
   • 인코더와 디코더
   • 독자이자 작가
   • 항상 일치하는 두 개의 동일한 사본
   • 가짜를 만드는 생성자와 그것을 판단하는 판별자
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. GAN에서 '모드 붕괴'란 무엇입니까?
   • 판별자는 모든 데이터를 삭제합니다.
   • 네트워크에 메모리가 부족합니다.
   • 생성기는 다양한 출력 대신 몇 가지 반복 출력만 생성합니다.
   • 이미지 해상도가 자동으로 두 배로 늘어납니다.
5. Which researchers introduced the GAN framework in the 2014 paper?
   • Alan Turing alone in 1950
   • 이안 굿펠로우(Ian Goodfellow)와 동료들
   • 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