GAN Neural

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GAN Neural

GAN Neural

Assessment

Quiz

Computers

University

Practice Problem

Hard

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12 questions

Show all answers

1.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

Gradient Ascent Network

Generative Adversarial Network

Global Activation Network

Generative Adversarial Node

2.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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

3.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

What are the two main components of a GAN?

generator and discriminator

encoder and decoder

classifier and regressor

producer and consumer

4.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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.

5.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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.

6.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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.

7.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

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.

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