Generative Adversarial Networks(Generative AI) QUIZ (MCQ QUESTIONS AND ANSWERS)

Total Correct: 0

Time:20:00

Question: 1

In the training process of GANs, what does the term "adversarial" refer to?

The competition between multiple discriminators

The collaboration between generator and discriminator

The attempt to minimize the generator's loss function

The adversarial relationship between the generator and discriminator

Question: 2

Which component of a GAN is responsible for generating new data samples?

Discriminator

Encoder

Generator

Classifier

Question: 3

Which regularization technique is commonly used to prevent mode collapse in GANs?

Dropout

L1 Regularization

L2 Regularization

Gradient Penalty

Question: 4

Which approach is used to stabilize GAN training by controlling the learning rate of the Generator and Discriminator?

Learning rate decay

Learning rate annealing

Learning rate scheduling

Learning rate adjustment

Question: 5

What is the main limitation of using GANs for image generation tasks?

Limited scalability to large datasets

High computational cost

Difficulty in training stable models

Inability to generate high-resolution images

Question: 6

In which stage of GAN training does mode collapse occur?

Early stage

Middle stage

Late stage

Throughout the training process

Question: 7

Which technique is used to improve the training stability of GANs by penalizing the norm of gradient of the Discriminator?

Feature matching

Minibatch discrimination

Gradient penalty

Spectral normalization

Question: 8

What is the primary disadvantage of using Wasserstein GANs (WGANs) compared to traditional GANs?

Limited training stability

Longer training time

Higher computational cost

Difficulty in controlling mode collapse

Question: 9

Which variant of GANs is specifically designed for generating images with specific attributes?

Conditional GAN (cGAN)

Deep Convolutional GAN (DCGAN)

Wasserstein GAN (WGAN)

InfoGAN (Information Maximizing GAN)

Question: 10

How can GANs be used in the domain of anomaly detection?

By generating synthetic anomalies for training

By classifying anomalies in existing data

By compressing anomaly data

By ignoring anomaly detection tasks

Question: 11

What is the main drawback of using GANs for image generation tasks?

Limited scalability to large datasets

High computational cost

Difficulty in training stable models

Inability to generate high-resolution images

Question: 12

Which technique is used to ensure that the Generator generates diverse samples instead of collapsing to a single mode?

Diversity regularization

Data augmentation

Latent space interpolation

Minibatch discrimination

Question: 13

Which optimization algorithm is commonly used to train Generative Adversarial Networks (GANs)?

Gradient Descent

Adam

RMSprop

AdaGrad

Question: 14

Which technique is used to stabilize GAN training by controlling the learning rate of the Generator and Discriminator?

Learning rate decay

Learning rate annealing

Learning rate scheduling

Learning rate adjustment

Question: 15

Which approach is used to overcome the vanishing gradients problem in GANs?

Skip connections

Stochastic gradient descent

Batch Normalization

Feature matching

Question: 16

What is the primary purpose of Generative Adversarial Networks (GANs)?

Image classification

Image generation

Image compression

Image segmentation

Question: 17

In which stage of the GAN training process does mode collapse often occur?

Early stage

Middle stage

Late stage

Throughout the training process

Question: 18

What problem does the use of Batch Normalization in GANs aim to address?

Mode collapse

Vanishing gradients

Exploding gradients

Training instability

Question: 19

What is the main advantage of using Wasserstein GANs (WGANs) over traditional GANs?

Improved training stability

Faster convergence

Lower computational cost

Better scalability

Question: 20

Which variant of GANs is specifically designed for generating high-quality images?

DCGAN (Deep Convolutional GAN)

WGAN (Wasserstein GAN)

InfoGAN (Information Maximizing GAN)

CycleGAN

Question: 21

Which of the following is a potential application of Generative Adversarial Networks (GANs)?

Image classification

Image generation

Speech recognition

Natural language processing

Question: 22

What is the training process of a Generative Adversarial Network (GAN) often described as?

Unsupervised learning

Supervised learning

Semi-supervised learning

Reinforcement learning

Question: 23

What is the objective of the Discriminator in a Generative Adversarial Network (GAN)?

To minimize the loss function of the Discriminator

To maximize the loss function of the Discriminator

To minimize the loss function of the Generator

To maximize the loss function of the Generator

Question: 24

What is the objective of the Generator in a Generative Adversarial Network (GAN)?

To minimize the loss function of the Discriminator

To maximize the loss function of the Discriminator

To minimize the loss function of the Generator

To maximize the loss function of the Generator

Question: 25

In a Generative Adversarial Network (GAN), what loss function is typically used to train the Discriminator?

Cross-Entropy Loss

Mean Squared Error (MSE) Loss

Binary Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Question: 26

In a Generative Adversarial Network (GAN), what loss function is typically used to train the Generator?

Cross-Entropy Loss

Mean Squared Error (MSE) Loss

Binary Cross-Entropy Loss

Kullback-Leibler (KL) Divergence

Question: 27

What is the role of the Discriminator in a Generative Adversarial Network (GAN)?

To generate fake data samples

To evaluate the authenticity of data samples

To classify data samples

To preprocess input data

Question: 28

What is the role of the Generator in a Generative Adversarial Network (GAN)?