GAN training loop in PyTorch - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does GAN stand for and what are its two main parts?

GAN stands for Generative Adversarial Network. It has two main parts: the Generator, which creates fake data, and the Discriminator, which tries to tell real data from fake data.

beginner

Why do we train the Discriminator and Generator alternately in a GAN training loop?

We train the Discriminator to better tell real from fake data, and then train the Generator to fool the Discriminator. Alternating helps both improve together.

beginner

In a GAN training loop, what is the purpose of the noise vector input to the Generator?

The noise vector is random input that the Generator uses to create diverse fake data samples.

intermediate

What loss function is commonly used to train the Discriminator in a GAN?

Binary Cross-Entropy loss is commonly used to train the Discriminator to classify real vs fake data.

beginner

What is the main goal of the Generator during GAN training?

The Generator aims to produce fake data that the Discriminator classifies as real, effectively 'fooling' it.

In a GAN training loop, what do we update first?

A Neither, just evaluate

B Generator weights

C Both at the same time

D Discriminator weights

We first update the Discriminator to better classify real and fake data before updating the Generator.

What input does the Generator receive to create fake data?

A Random noise vector

B Labels of real or fake

C Discriminator output

D Real data samples

The Generator uses a random noise vector as input to generate diverse fake samples.

Which loss function is typically used for training the Discriminator in GANs?

A Mean Squared Error

B Categorical Cross-Entropy

C Binary Cross-Entropy

D Hinge Loss

Binary Cross-Entropy loss is commonly used for the Discriminator to classify real vs fake.

What does the Generator try to maximize during training?

A Discriminator's ability to detect fake data

B Discriminator's error on fake data

C Loss of the Discriminator on real data

D Distance between real and fake data

The Generator tries to maximize the Discriminator's error on fake data, meaning it wants to fool the Discriminator.

Why do we detach the fake data when training the Discriminator?

A To prevent gradients flowing back to the Generator

B To speed up training

C To save memory

D To normalize the data

Detaching fake data prevents gradients from flowing back to the Generator when training the Discriminator.

Describe the steps of a typical GAN training loop.

Think about how the Generator and Discriminator take turns learning.

Explain why alternating training between Generator and Discriminator is important in GANs.

Consider the competition between the two parts.