Reference Figure 1.

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

Question

Reference Figure 1.

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

BlackTom AI · Accepted Answer

To determine the appropriate GAN training objective from the options, we need to consider how the two cost functions typically behave during training and which one is commonly used for stable learning.
Option 1: "Both Saturating and Non-Saturating." This choice claims that both cost formulations are used or appropriate. In practice, while both can be defined, training GANs almost exclusively utilizes the non-saturating variant for the generator to avoid vanishing gradients. Therefore, this option is not correct as a singular recommended choice for training.
Option 2: "Non-Saturating Cost (Green)." The non-saturating (non-saturating) loss for the generator is designed to provide stronger gradients when the discriminator is performing well, which helps the generator learn more effectively and stabilizes training in many GAN setups. This aligns with common practice where the generator aims to maximize log(D(G(z))) rather than minimizing log(1 - D(G(z))). The caption indicates the green curve corresponds to this beneficial non-saturating objective, making this the sensible choice.
Option 3: "Saturating Cost (Blue)." The saturating (original) generator loss can lead to vanishing gradients when the discriminator becomes strong, making it harder for the generator to improve. This often results in slower or unstable training, which is why this option is generally not preferred for training GANs in practice.
In summary, the non-saturating cost is favored for its improved gradient signal during training, while the saturating version tends to hinder progress. The mixed statement in option 1 does not reflect the typical single recommended choice for training. Therefore, option 2 is the appropriate selection.

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

类似问题

Which statement about Generative Adversarial Networks (GANs) is TRUE?

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

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

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?

更多留学生实用工具

智能学习助手

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