Answers.Body: <p>I think so you are new to GANs. </p>
  
  <p>As the name itself implies, the generator task is to generate new images to match the distribution of the model and the input data, hence it is the generator that contributes in the generation of the data, while the discriminator just tries to differentiate between the generated images by the generator and real data.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-06-15T02:20:51.940Z
Answers.LastActivityDate: 2020-06-15T10:04:51.797Z
Answers.LastEditDate: 2020-06-15T10:04:51.797Z
Answers.UserDisplayName: nancy
Body: <p>After a GAN is trained, which parts of it are used to generate new outputs from data?</p>
  
  <p>Options are:</p>
  
  <ol>
  <li>Neither</li>
  <li>Discriminator</li>
  <li>Generator</li>
  <li>Both Generator and Discriminator</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2020-06-14T20:35:39.630Z
Comments.Text: Is this being asked as part of coursework? If so, could you add any insight or opinion that you already have? Use [edit] to add details.
Comments.UserDisplayName: Neil Slater
Tags: <machine-learning><generative-adversarial-networks><homework>
Title: After a GAN is trained, which parts of it are used to generate new outputs from data?
UserDisplayName: MILLIONISH
ThreadText: # After a GAN is trained, which parts of it are used to generate new outputs from data?
  
  (Asked by: username_0 on 2020-06-14)
  
  After a GAN is trained, which parts of it are used to generate new outputs from data?
  
  Options are:
  
  1. Neither
  2. Discriminator
  3. Generator
  4. Both Generator and Discriminator
  
  **Tags:** machine-learning, generative-adversarial-networks, homework
  
  > **Comments:**
  > **username_1**: Is this being asked as part of coursework? If so, could you add any insight or opinion that you already have? Use [edit] to add details.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2020-06-15. Score: 0
  
  I think so you are new to GANs.
  
  As the name itself implies, the generator task is to generate new images to match the distribution of the model and the input data, hence it is the generator that contributes in the generation of the data, while the discriminator just tries to differentiate between the generated images by the generator and real data.
  
  
  ---
  
  