Generative Adversarial Network Multiple Choice Questions

Hello Friends in this post we will discuss about Generative Adversarial Network machine learning Multiple Choice Questions and answers.

1. GNN Stands for ______.
  1. Generative Advertising Network
  2. Generative Adversarial Network
  3. Generate Adversarial Network
  4. Generation adversarial Network

Generative Adversarial Network

2.Generative Adversarial Networks was developed and introduced by_________.
  1. Allan Tunning
  2. J. Goodfellow
  3. Rutherford
  4. None of the above

J. Goodfellow

3.Generative Adversarial Networks was developed and introduced in ________.
  1. 2015
  2. 2014
  3. 2013
  4. 2012

2014

4.Generative Adversarial Networks (GANs) can be broken down into ______ parts.
  1. 4
  2. 3
  3. 2
  4. 1

Generative Adversarial Networks (GANs) can be broken down into three parts.

5._____ is used To learn a generative model, which describes how data is generated in terms of a probabilistic model.
  1. Adversarial
  2. Generative
  3. Networks
  4. discriminator

Generative

6.In ______The training of a model is done in an adversarial setting.
  1. Adversarial
  2. Generative
  3. Networks
  4. Discriminator

Adversarial

7.In ______Use deep neural networks as the artificial intelligence (AI) algorithms for training purpose.
  1. Adversarial
  2. Generative
  3. Networks
  4. Discriminator

Networks

8.______is the simplest type GAN.
  1. Conditional GAN
  2. Vanilla GAN
  3. Deep Convolutional GAN
  4. Laplacian Pyramid GAN

Vanilla GAN

9.______described as a deep learning method in which some conditional parameters are put into place.
  1. Conditional GAN
  2. Vanilla GAN
  3. Deep Convolutional GAN
  4. Laplacian Pyramid GAN

Conditional GAN

10.Which is one of the most popular also the most successful implementation of GAN?
  1. Conditional GAN
  2. Vanilla GAN
  3. Deep Convolutional GAN
  4. Laplacian Pyramid GAN

Deep Convolutional GAN

11.____is a way of designing a GAN in which a deep neural network is used.
  1. Conditional GAN
  2. Vanilla GAN
  3. Deep Convolutional GAN
  4. Super Resolution GAN

Super Resolution GAN

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Application of Machine Learning Multiple Choice Questions and answers

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