Reference Figure 1.

Typically early in training, the value of D(G(z)) is closer to which of the following values?

Question

Reference Figure 1.

Typically early in training, the value of D(G(z)) is closer to which of the following values?

BlackTom AI · Accepted Answer

Question: Reference Figure 1. Typically early in training, the value of D(G(z)) is closer to which of the following values?

Option 1: Close to 1
- Interpretation: This would mean the discriminator (D) confidently thinks the generator’s output G(z) is real. In the early stages of GAN training, the generator is typically not yet producing realistic samples, so the discriminator easily identifies fake outputs as not real. Therefore this option is unlikely.

Option 2: Close to 0
- Interpretation: D(G(z)) represents the discriminator’s estimate that G(z) is real. If the generator is weak early on, D should correctly label its outputs as fake, pushing D(G(z)) toward 0. This aligns with the standard dynamic where the generator has not yet learned to fool the discriminator.

Option 3: Close to 0.5
- Interpretation: A value near 0.5 suggests the discriminator is uncertain or guessing randomly, offering no real signal about real vs fake. Early in training, the discriminator usually has a strong signal distinguishing fake from real, so 0.5 would not be typical.

Summary of reasoning: At the start of training, the generator produces poor approximations of real data, so the discriminator easily identifies these as fake. Consequently, D(G(z)) tends toward 0, reflecting low probability that G(z) is real. The other values would imply either the discriminator is almost always fooled (close to 1) or that it is guessing randomly (close to 0.5), neither of which matches the typical early-stage behavior of GAN training.

CS-7643-O01, OAN, OSZ Quiz #5: Module 4

Reference Figure 1. Typically early in training, the value of D(G(z)) is closer to which of the following values?

查看解析

登录即可查看完整答案

类似问题

Reference Figure 1. You know that your GAN is trained when D(G(z)) is close to 1

Reference Figure 1. Two cost functions are presented in the figure, which one would you use to train your GAN?

Generative adversarial networks (GANs) learn a parameterized density function, p(x).

Why did Elgammal switch from Generative Adversarial Networks (GAN) to Creative Adversarial Networks (CAN) to create variations of the images he collected for AI artwork development?

Which variation of GANs uses two discriminators: one as a typical binary classifier and the other as a score function?

What is the primary objective of the discriminator in a GAN during training?

What are the two networks that make up a GAN?

Why were GANs designed to avoid using Markov chains?

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单