GAN Neural

What does GAN stand for in the context of neural networks?

• Gradient Ascent Network

• Generative Adversarial Network

• Global Activation Network

• Generative Adversarial Node

Explain the basic architecture of a GAN.

• The basic architecture of a GAN includes a generator and a discriminator trained in a minimax game framework.

• The basic architecture of a GAN consists of a single neural network instead of two

• A GAN architecture involves only a discriminator without a generator

• In a GAN, the generator and discriminator are not trained simultaneously

What are the two main components of a GAN?

• generator and discriminator

• encoder and decoder

• classifier and regressor

• producer and consumer

How does the generator in a GAN generate new data?

• By using pre-defined templates

• By copying existing data

• By analyzing user behavior

• By taking random noise as input and passing it through a neural network.

What is the role of the discriminator in a GAN?

• The discriminator distinguishes between real and fake data.

• The discriminator preprocesses the input data.

• The discriminator optimizes the generator in a GAN.

• The discriminator generates fake data.

What is mode collapse in GAN training?

• Random fluctuations in generated samples leading to convergence on a wide range of modes in the data distribution.

• Perfect balance in generated samples leading to convergence on a single mode in the data distribution.

• Limited diversity in generated samples leading to convergence on a few modes in the data distribution.

• Increased diversity in generated samples leading to convergence on all modes in the data distribution.

How does the training process of GANs differ from traditional neural networks?

• The training process of GANs does not involve any neural networks, while traditional neural networks are solely based on neural networks.

• The training process of GANs uses reinforcement learning, while traditional neural networks use unsupervised learning.

• The training process of GANs involves only one neural network, while traditional neural networks involve multiple networks.

• The training process of GANs involves two neural networks trained adversarially, while traditional neural networks are trained to minimize a specific loss function.

What are some common challenges faced during the training of GANs?

• Mode collapse, vanishing gradients, instability, hyperparameter tuning

• Overfitting, underfitting, convergence issues

Explain the concept of adversarial loss in GANs.

• Adversarial loss in GANs is the loss function that encourages the generator to produce unrealistic samples.

• Adversarial loss in GANs is the loss function that promotes collaboration between the generator and discriminator networks.

• Adversarial loss in GANs is the loss function that encourages the discriminator to produce realistic samples.

• Adversarial loss in GANs is the loss function that drives the competition between the generator and discriminator networks by encouraging the generator to produce realistic samples.

What is the purpose of the generator loss function in GANs?

• To increase the discriminator's accuracy

• To decrease the training time of the generator

• To add noise to the generated samples

• To guide the generator towards producing realistic samples.