引导式测验 · 中等 等级

生成对抗网络测验

回顾生成器和判别器的角色、原始 GAN 输入、对抗性反馈、模式崩溃、历史起源、均衡和调节。

相关引导路径生成对抗网络
问题 1 的 8

GAN 中的两个竞争网络是什么？

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  1. GAN 中的两个竞争网络是什么？
    • 编码器和解码器
    • 一个读者和一个作家
    • 两个相同的副本始终一致
    • 生成赝品的生成器和判断赝品的鉴别器
  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. 什么是 GAN 中的“模式崩溃”？
    • 鉴别器删除所有数据
    • 网络内存不足
    • 生成器仅产生少量重复输出，而不是多样性
    • 图像分辨率自动加倍
  5. Which researchers introduced the GAN framework in the 2014 paper?
    • Alan Turing alone in 1950
    • 伊恩·古德费洛和同事
    • 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