What is the primary objective of a GAN's generator?

• To classify images

• To detect anomalies

• To generate realistic data that can fool the discriminator

• To calculate loss functions

Which of the following is NOT a role of the discriminator in a GAN?

• Distinguish real data from fake data

• Provide gradient feedback to the generator

• Generate new data samples

• Improve over time to detect better fakes

What kind of loss is used in the original GAN paper for the generator?

• Mean Squared Error

• Binary Cross-Entropy

• Hinge Loss

• Wasserstein Loss

What is 'mode collapse' in GANs?

• The generator learns the entire data distribution

• The discriminator becomes too weak

• The generator produces limited types of outputs

• The training fails due to overfitting

Which of the following helps in improving GAN training stability?

• Decreasing the number of epochs

• Using ReLU in the discriminator

• One-sided label smoothing

• Removing the discriminator

What architecture does DCGAN use in place of fully connected layers?

• RNN layers

• LSTM layers

• Convolutional layers

• Transformer blocks

What does a transposed convolution layer do in a GAN generator?

• Reduces spatial resolution

• Acts as a pooling layer

• Upsamples feature maps

• Computes gradients

In a Conditional GAN, how is the class label typically fed into the generator?

• As an output

• Ignored

• Concatenated with the input noise

• Used to initialize weights

What is the key idea behind CycleGAN?

• Supervised learning from labeled pairs

• Learning mappings using conditional labels

• Unsupervised image-to-image translation using cycle consistency

• Reducing GAN parameters

Pix2Pix is best described as:

• A style transfer GAN without supervision

• A paired image-to-image translation GAN

• A text-to-image generator

• A classifier based on adversarial loss